We are anonymous, we are legion

Amazon launches Machine Learning-based platform for healthcare space

Admin — 2018-12-03T00:23:06Z

Amazon’s Comprehend Medical platform uses a new HIPAA-eligible machine learning service to process unstructured medical text and information such as dosages, symptoms and signs, and patient diagnosis.

The article by Kul Bhushan was published in the Hindustan Times on November 28, 2018.

With an objective to push deeper into the health space, Amazon has introduced a new Machine Learning (ML) software to analyse medical records for better treatments of patients and reduce overall expenditure.

Unveiled at the company’s re:Invent cloud conference in Las Vegas, Amazon’s Comprehend Medical platform uses a new “HIPAA-eligible machine learning service that allows developers to process unstructured medical text and identify information such as patient diagnosis, treatments, dosages, symptoms and signs, and more.”

“Comprehend Medical helps health care providers, insurers, researchers, and clinical trial investigators as well as health care IT, biotech, and pharmaceutical companies to improve clinical decision support, streamline revenue cycle and clinical trials management, and better address data privacy and protected health information (PHI) requirements,” explains the company on its website.

Amazon aims to mitigate the time spent on manually analysing medical data of a patient. The company hopes the software will ultimately empower users to make a more informed decision about their health and even things like scheduling care visits.

“Unlocking this information from medical language makes a variety of common medical use cases easier and cost-effective, including: clinical decision support (e.g., getting a historical snapshot of a patient’s medical history), revenue cycle management (e.g., simplifying the time-intensive manual process of data entry), clinical trial management (e.g., by identifying and recruiting patients with certain attributes into clinical trials), building population health platforms, and helping address (PHI) requirements (e.g., for privacy and security assurance.),” the company added.

Amazon also pointed out that some of the medical institutes such as Seattle’s Fred Hutchinson Cancer Research Center and Roche Diagnostics have already implemented the software.

Amazon’s expansion into the healthcare space comes after it acquired health-focused startup PillPack for $1 billion earlier this year. Apart from Amazon, other technology companies like Apple and Microsoft are investing into the healthcare space.

Apple is already offering HealthKit and CareKit platforms to develop apps focused on health. The company earlier this year launched Apple Watch Series 4 with ECG support. Microsoft, however, has deeper footprints in the health segment. The company is building a bunch of Artificial Intelligence-based tools for healthcare.

For instance, Microsoft’s Project InnerEye uses machine learning technology to build tools for automatic, quantitative analysis of three-dimensional radiological images.

According to various reports, Artificial Intelligence is going to make a big impact in the healthcare industry. An Accenture report in 2017 predicted that the AI apps can create $150 billion in annual savings for the United States alone.

Back in India, the adoption of AI in healthcare is growing. According to a report by the Centre for Internet and Society India, “the use of AI in healthcare in India is increasing with new startups and large ICT companies offering AI solutions for healthcare challenges in the country.”

Bengalure-based startup mfine has developed an AI-based healthcare platform which learns medical standards and protocols and diagnosis and treatment methods to further help the doctors with necessary data and analysis. The company earlier this year raised $4.2 million in funding.

For more details visit https://cis-india.org/internet-governance/news/hindustan-times-november-28-2018-kul-bhushan-amazon-launches-machine-learning-based-platform-for-healthcare-space

Amazon and Walmart are about to take a big hit in India

Admin — 2019-02-01T15:03:56Z

India is the world’s biggest emerging digital economy, and Silicon Valley’s top companies have invested huge sums to cash in on it. Now new regulations are threatening their business.

The blog post was published in Q13 Fox on January 31, 2019. Gurshabad Grover was quoted.

E-commerce restrictions due to go into effect Friday will prevent global retailers such as Amazon and Walmart from using their deep pockets and massive scale to drive down prices in India.

And proposed legal changes would require social media companies like Facebook and Twitter to monitor and take down content at the request of Indian authorities, which critics say could be misused for censorship.

The new rules highlight the risk global tech giants are running in a country they see as their next growth frontier.

Amazon and Walmart push back

The new e-commerce rules, announced in late December, look to curb practices like steep discounts that have helped Amazon dominate the US market and already make huge inroads in India.

The rules state that foreign online retailers can no longer strike deals with companies to offer products that are not available elsewhere. They also prevent these platforms from selling products distributed by companies they have invested in.

That would strike at the heart of Amazon’s business in India — the US company has snapped up stakes in several local suppliers.

Amazon and India’s biggest online retailer, Walmart-owned Flipkart, had been pushing India to delay the introduction of the new rules, but the government said in a statement Thursday that it had decided “after due consideration” not to do so.

Amazon had written to the Indian government asking for a four-month extension to comply with the new rules, a company spokesperson said.

“With over [400,000] sellers and hundreds of thousands of transactions happening daily on the Amazon India Marketplace we need adequate time to understand the details of the policy,” the spokesperson added.

Flipkart had asked the government for a six-month extension, a person familiar with the matter told CNN.

The company reportedly warned of “significant customer disruption” if the new policy is implemented this week. Flipkart CEO Kalyan Krishnamurthy said in a letter to the Indian government that the new rules could “have undesirable impacts on the continued growth of e-commerce in India,” according to Reuters.

The rules will affect the Bangalore-based company’s sales of products like smartphones, many of which it offers exclusively to its customers.

Flipkart declined to comment.

The changes follow intensive lobbying by India’s small businesses against Amazon and Walmart’s outsized influence in the country. (Together they have more than 70% of the Indian online shopping market.) The Confederation of All India Traders, which says it represents more than 70 million local retailers, warned the government against granting an extension.

“If any deferment or extension is given, the small traders both offline and online will be compelled to resort to a national campaign against any such move … which may also have political repercussions,” the group said in a statement earlier this week, a thinly-veiled warning to the government in an election year.

The trade body expressed “deep satisfaction” at the government’s decision not to extend the deadline.

India’s WhatsApp problem

India’s effort to further regulate its internet isn’t restricted to retail.

In late December, two days before the e-commerce restrictions were unveiled, India’s technology ministry published a host of proposed changes to laws governing online content.

The changes state that “intermediaries” including internet providers and platforms like Facebook and Twitter must remove “unlawful” material within 24 hours at the request of Indian authorities. That covers content that goes against India’s sovereignty, national security or foreign relations as well as “public order, decency or morality,” the new rules state.

Experts say the broad phrasing leaves the rules open to misuse and could be used to suppress free speech. It could also lead to self censorship by tech companies to avoid government scrutiny.

“It can lead to en masse takedown of content,” said Gurshabad Grover, a policy officer at the Bangalore-based Centre for Internet and Society, a think tank. “Intermediaries are often happy to take down perfectly legal content just to avoid liability.”

Twitter said in a statement that it would continue to lobby the Indian government on the proposed regulations before they are passed into law.

“Our hope is that after this robust public consultation process any changes to the [rules] in India strike a careful balance that protects important values such as freedom of expression,” a Twitter spokesperson told CNN Business.

Google and Facebook declined to comment.

The Asia Internet Coalition, an industry group that counts all three companies as its members, also urged the Indian government to reconsider the rule changes.

“In addition to interfering with the fundamental rights of freedom of speech and expression, and right to privacy as guaranteed under the constitution, the [proposed regulations] impose burdensome obligations on the intermediaries,” the group said in a letter to India’s technology ministry.

The regulations appear to be driven by India’s growing battle with fake news and misinformation, with viral rumors on Facebook’s mobile messaging service WhatsApp blamed for more than a dozen lynchings last year.

The Indian government specifically called out the mob violence in its statement announcing the regulations, citing the “misuse of social media by criminals and anti-national elements” as a key factor.

The rules also state that platforms must enable the tracing of individual posts and messages at the government’s request, a requirement that WhatsApp has previously rejected as a non-starter.

India is WhatsApp’s biggest market, with more than 200 million users.

Why India matters

India has opened itself up to foreign investment in recent years, particularly in its fast-growing tech and retail industries, and companies from around the world have rushed in.

India’s internet is a particularly tempting prize. About 500 million people are already online, with nearly 900 million more yet to be connected.

Amazon has pledged at least $5 billion dollars to growing its business in the country, while Walmart spent $16 billion to buy Flipkart last year.

Facebook and Google have also identified India as their next big market, rolling out several features and services in the country before taking it to the rest of the world.

But the changing legal environment presents a huge challenge, and Big Tech is battling to keep the promise alive.

“An uncertain, constantly changing regulatory environment is not good for any business,” Mishi Choudhary, legal director at the New York-based advocacy group Software Freedom Law Center, told CNN.

“India must decide where it sees itself in the global landscape,” Choudhary added. “It can either be a democracy that will let the best company win and provide an open, free and secure internet to its citizens or turn the way our neighbors across the Himalayas [China] have. It can’t have it both ways.”

For more details visit https://cis-india.org/internet-governance/news/q-13-fox-january-31-2019-amazon-and-walmart-are-about-to-take-a-big-hit-in-india

Almost every social network has a porn problem—so why is India banning only TikTok?

Ananya Bhattacharya — 2019-04-28T04:46:36Z

TikTok is in some serious trouble in India. Though it isn’t the only one battling a rising tide of porn, violence, and fake news, the country’s courts have singled out the Chinese video-sharing app for a ban.

The article by Ananya Bhattacharya was published in Quartz India. Shweta Mohandas was quoted.

Incumbents such as Twitter, WhatsApp, and Facebook have been fighting the same problems as TikTok has, but they haven’t yet faced such extreme measures. Several experts have called the Madras high court ban on the world’s most popular video app a “kneejerk reaction” that is “outsized.”

“While it is important to place appropriate curbs on porn and trolling, this decision sounds extreme since there are myriad ways to spread the two vices,” Anindya Ghose, the Heinz Riehl professor of business at New York University’s Stern School, told Quartz.

So why is TikTok bearing the brunt?

Singled out?

Unlike TikTok, most other social media and video/photo sharing apps are largely private and controlled in nature, which means a post can only be seen by a limited network of a user. “The issue with TikTok is it encourages interactivity over videos,” said Prasanto Roy, a New Delhi-based tech policy consultant. “Facebook and Instagram start out with smaller circles or friends groups, while TikTok can get to a larger audience quickly—including allowing video interaction with strangers.”

Given that TikTok is much younger to most others in the segment, the app lacks certain safeguards to ensure users’ safety. For instance, there are no checks & balances on the app to moderate user-generated content, experts told Quartz.

“Other social media (platforms) pro-actively filter out content that violate their community guidelines,” Shweta Mohandas, policy officer at the Centre for Internet and Society of India (CIS), told Quartz. “The fact that TikTok’s community guidelines’ reporting mechanism is based on user reports could be a reason why the porn problem has become more prevalent.”

Although some cases slip through the gaps, Facebook and Google are proactively blocking search terms related to child porn. Facebook and Instagram are also using sophisticated artificial intelligence (AI) tools to detect revenge porn. Vernacular-language social network Sharechat also has algorithms that flag illicit content. “YouTube gives you policies and they remove your video or puts a strike against your account, but there’s no such rule or guideline on TikTok,” Indian YouTuber and TikTok-user Kulbushan Kundalwal said in a videoreacting to the ban.

It has been trying to strengthen its systems lately.

The app, which lets users create and post 15-second-long videos, has set up a team of content moderators who speak a number of regional languages. It has also hosted awareness drives featuring celebrities. However, it relies mostly on user reports.

Clearly, these measures are not enough and millions of its Indian users—almost 40% of the app’s 500 million user base—remain exposed to risks.

In the US, too, TikTok was berated based on similar concerns. It recently settled with the Federal Trade Commission, paying $5.7 million (Rs40 crore), the largest civil penalty ever collected in a child privacy violation case in the US.

Will this ban help?

Taking the app down can slam the brakes on its meteoric rise, but won’t kill it. Banning one app won’t change user behaviour, experts said.

For starters, users who have it installed can continue using it. And though it’s not directly available for download from the app store, there are ways to get it. In fact, getting on the app is easy: Any existing user can share the app with others through platforms like ShareIt.

TikTok is not a unique proposition.

There are also several third-party app stores, other than those of Google and Apple—apkpure, androidapkbox, and uptodown, among others—where TikTok is still available.

In any case, it is not the only source of dangerous content online. “TikTok is not a unique proposition,” Gurugram-based market research firm techARC said in a note. The ban has already begun to boost similar apps.

For instance, since April 16, around the time when news of a likely ban on TikTok in India began floating, video-sharing app Like has been trending as the third most popular in India on analytics site App Annie’s charts, techARC found.

“There is a need to have a holistic approach to getting rid of such digital menace that cannot be absolved by technology or legal recourse alone,” techARC founder and chief analyst Faisal Kawoosa said.

For more details visit https://cis-india.org/internet-governance/news/ananya-bhattacharya-quartz-india-april-19-2019-india-bans-tiktok-over-porn-but-not-facebook-twitter-instagram

Allow admins to add users to online group chats only after permission: SFLC.in

Admin — 2018-06-26T02:03:19Z

SFLC.in -- a donor supported legal services organisation -- has written an open letter to messaging service providers like WhatsApp, Facebook and others, urging them to modify their platforms to ensure that users are not added to group chats without their permission.

The article was published in the Times of India on June 1, 2018.

In its letter, Software Freedom Law Centre, India (SFLC.in) said any user with administrator rights can currently add another person to the group without the latter's permission.

In the absence of a mechanism to prevent themselves from being added to groups that they would not like to participate in, users have no option but to manually exit the groups.

"This is a troubling state of affairs because users may be forcefully exposed to a range of subjectively undesirable content that they would never have signed up for otherwise, which can be particularly damaging, especially in situations where malicious actors attempt to intimidate, disparage, harass or harm individuals in any way," the letter said.

The letter has also been co-signed by Digital Empowerment Foundation, Centre for Internet and Society and a few others.

Emails sent to Facebook, Tencent and others seeking their comments did not elicit any response.

SFLC.in argued that the current format is an issue for many, including those belonging to minority and vulnerable groups.

"While blocking malicious actors can usually help mitigate the damage to an extent, on online messaging services like yours, they are able to easily circumvent blocks by creating groups and adding their targets to these groups," it said.

The problem is only made worse when personal information like phone numbers, user IDs and photographs are shared with a large number of users, which could open the doors to even greater abuse, it added.

SFLC.in argued that there are no adequate safeguards to prevent infringement of user rights and therefore, these platforms should take immediate steps to address this issue.

"...implement measures to make it so that being added to group conversations without permission is no longer a possibility. Not only will this greatly help in limiting abusive uses of your services, but it will also make users less wary of using the services, making the Internet a safer space for us all," it added.

For more details visit https://cis-india.org/internet-governance/news/times-of-india-june-1-2018-allow-admins-to-add-users-to-online-group-chats-only-after-permission-sflc-in

All India Privacy Symposium

Natasha Vaz — 2012-03-01T06:16:53Z

Are we citizens or subjects? Experts gather in Delhi for public symposium on privacy, transparency, e-governance and national security in India.

Following 18 months of research by Privacy India, the Centre for Internet and Society and the Society in Action Group, with support from London-based Privacy International, the groups today held an All India Privacy Symposium at the India International Centre in New Delhi. Speakers included Supreme Court Advocate Menaka Guruswamy, Microsoft Director of Corporate Affairs Deepak Maheshwari, social researcher and activist Usha Ramanathan, journalist Saikat Datta and former Chief of RAW Hormis Thorakan.

A few themes recurred across all five panels (Privacy and Transparency, Privacy and E-Governance Initiatives, Privacy and National Security, Privacy and Banking, and Privacy and Health). Perhaps the most prominent was the repeated allegation that the Indian government' technological illiteracy is putting its citizens at risk. One panelist described how an RTI request had recently revealed that the government had no idea how many of its own computers had been hacked or how much data had been stolen – even though this information has been in the public domain since the Wikileaks diplomatic cable releases.

The increased use of public-private partnerships and outsourcing was also a major cause for concern. Public money is being funneled into privately-held commercial enterprises – which, unlike public bodies, are not subject to RTI requests – and spent on e-governance initiatives like UID. Social researcher Anant Maringati spoke of a "hybrid world" in which government projects were fulfilled by completely unaccountable private actors. Advocate Malavika Jayaram remarked that, while private companies tend to have far greater technological expertise than government officials, they are ultimately motivated by profit rather than public benefit; we should therefore ask ourselves whether they can really be trusted with our information.

Government surveillance for the purposes of crime prevention also came under scrutiny, when Saikat Datta described how he himself had been put under illegal surveillance by an unauthorized intelligence agency. He warned of the dangers of excessive wiretapping, a practice that currently generates such a “mountain” of information that anything with real intelligence value tends to be ignored until it is too late, as happened with the Mumbai bombings in 2008. It is clear that the Indian government’s surveillance and interception programmes far exceed what is necessary for legitimate law enforcement.

Overall, panelists at the conference painted a vivid picture of India as a state that has made a habit of invading the privacy of individuals on a massive scale in the name of public benefit and law enforcement. Yet there is a clear sense that the benefits to society are not outweighing the costs to the individual. As Usha Ramanathan commented: “The question is, do we think of ourselves as citizens – or as subjects?”

See the webcast of the event here

For more details visit https://cis-india.org/internet-governance/all-india-privacy-symposium

All India Privacy Symposium

praskrishna — 2012-02-27T11:08:32Z

Privacy India in partnership with the International Development Research Centre, Canada, Society in Action Group, Gurgaon, Privacy International, UK and Commonwealth Human Rights Initiative is organizing the All India Privacy Symposium at the India International Centre, New Delhi on Saturday, February 4, 2012.

Since June 2010, Privacy India has been engaging in discussions with policy makers, the public and sectoral experts about privacy in India. The discussions have ranged from topics of identity and privacy, to minority rights and privacy, and consumer privacy. The findings of our research show that privacy was a neglected area of study for India in the past, however, this is changing. Advancements in technology, the introduction of e-governance initiatives like the National Fibre Optic Network, the introduction of new legislations, and debates surrounding national security, have brought privacy debates to the forefront in India. Although currently sectoral legislation deals with privacy issues, e.g., the Telegraph Act or RBI guidelines for banking, India has just begun to consider a horizontal legislation that deals comprehensively with privacy across all contexts. This conference is an opportunity to look forward to what could be the future scope of privacy in India.

Privacy India was set up in collaboration with the Centre for Internet and Society, Bangalore and Society in Action Group, Gurgaon, under the auspices of an international organization ‘Privacy International’. Privacy International is a non-profit group that provides assistance to civil society groups, governments, international and regional bodies, the media and the public in a number of countries. For more info, visit its website.

This is a public meeting. For participation in the event, get in touch with Elonnai (elonnai@cis-india.org)

Symposium Advisors

Sunil Abraham, Centre for Internet &Society (www.cis-india.org)
Rajan Gandhi, Society in Action Group
Phet Sayo, IDRC (www.idrc.org)
Gus Hosein, Privacy International (www.privacyinternational.org)
Sudhir Krishnaswamy, Centre for Law and Policy Research, Bangalore (www.clpr.org.in)
Vickram Crishna, Privacy International (www.privacyinternational.org)

Agenda

09:30- 10:00	Registration
10:00- 10:15	Welcome & Introduction to Privacy India Elonnai Hickok (Policy Advocate, Privacy India)
10:15- 10:30	Tea Break
10:30- 11:30	Panel I: Privacy and Transparency Moderator: Sunil Abraham (Executive Director, Centre for Internet & Society) Panelists: Prashant Bhushan (Senior Advocate, New Delhi), Simon Davies (Director General, Privacy International, UK), Ponnurangam K (Assistant Prof, IIIT New Delhi), Chitra Ahanthem (Journalist, Imphal), Aruna Roy (Social & Political Activist), Deepak Maheshwari (Director Corporate Affairs, Microsoft) Poster:Srishti Goyal (Law Student)
11:30- 12:30	Panel II: Privacy and E-Governance Initiatives Moderator: Sudhir Krishnaswamy (Professor, Azim Premji University) Panelists: Anant Maringanti (Independent Social Researcher), Usha Ramanathan (Advocate&Social Activist), Ram Sewak Sharma (Director General, UIDAI), Gus Hosein (Executive Director, Privacy International, UK), R K Singh (Union Home Secretary, New Delhi), Apar Gupta (Advocate, Supreme Court of India) Poster: Adrija Das (Law Student)
12:30- 13:30	Lunch
13:30- 14:30	Panel III: Privacy and National Security Moderator: Justice A P Shah (Former Chief Justice, Delhi High Court)* Panelists: Menaka Guruswamy (Advocate, Supreme Court, New Delhi), Amol Sharma (Journalist, Wall Street Journal)*, Saikat Datta (Journalist, DNA), Eric King (Human Rights and Technology Advisor, Privacy International, UK), Prasanth Sugathan (Legal Counsel, Software Freedom Law Center) and Oxblood Ruffin (Cult of the Dead Cow Security and Publishing Collective) Poster: Suchithra Menon (Law Student)
14:30- 15:30	Panel IV: Privacy and Banking Moderator: Prashant Iyengar (Associate Professor, Jindal Law University) Panelists: M R Umarji (Chief Legal Advisor, IBA), N A Vijayashankar (Cyber Law Expert), Sucheta Dalal (Managing Editor, MoneyLife Magazine)*, Malavika Jayaram (Advocate, Bangalore) Poster: Malavika Chandu (Law Student)
15:30- 15:45	Tea Break
15:45- 16:45	Panel V: Privacy and Health Moderator: Ashok Row Kavi (Journalist & LGBT Activist) Panelists: K K Abraham (President, Indian Network for People with HIV), Shri Sayan Chatterjee (Secretary, National Aids Control Organization), Dr V M Katoch (Secretary, Department of Health Research), Dr B S Bedi (Advisor, CDAC & Media Lab Asia) Poster: Danish Sheikh (Alternative Law Forum)
16:45- 17:00	The Way Forward Elonnai Hickok (Policy Advocate, Privacy India)

Bios of Speakers

Usha Ramanathan

Usha Ramanathan is an internationally recognized expert on the jurisprudence of law, poverty and rights. She writes and speaks on leading issues like the Bhopal gas leak tragedy, mass displacement, civil liberties, criminal law, environment and the judicial process. She is involved in the UID project and has written and debated extensively on it. She is a member of Amnesty International's Advisory Panel on Economic, Social and Cultural Rights and has been called upon by the World Health Organisation as a expert on mental health on various occasions. Her writings can be found at http://www.ielrc.org/.

NA.Vijayashankar

NA.Vijayashankar, more popularly known as Naavi, is a Techno Legal Information Security Consultant based in Bangalore, India. Naavi is a pioneer in the field of Cyber Law in India. He is the author of the first book (1999) and first E-Book (2003) on Cyber Laws in India. He has also authored a book titled “Cyber Laws, Corporate Mantra for the Digital Era”, “Cyber Laws Demystified” and “Cyber Laws for Engineers” as well as a book on Cyber Crimes in Kannada.

Naavi is the founder of www.cyberlawcollege.com which is the pioneering virtual educational institution in India dedicated to Cyber Law Education. Cyber Law College presently conducts offline and virtual courses on Cyber Laws. It has conducted several courses in association with law colleges in Karnataka such as KLE Law College, Bangalore, JSS Law College, Mysore, SDM law college Mangalore and KLE Law College Hubli.

Naavi is also the founder of www.naavi.org the premier Cyber Law Portal in India. Naavi has been engaged in the training of Police in Tamil Nadu and Karnataka and conducts several courses in Cyber Laws for different audiences. He has been a guest faculty in a number of institutions including NPA, IDRBT, DTRI, ISACA, NADT, LBS National Academy, Judicial Academies, NALSAR, etc., as well as several law, engineering and management institutions.

Naavi has over three decades of senior Corporate executive experience behind him. He has been an ex-Banker and Consultant to several Companies in IT Services. He has conducted hundreds of training sessions to professionals of various disciplines such as bankers, lawyers, chartered accountants, engineers, software professionals, police and judicial officers through workshops and in-house training programmes in cyber laws, cyber crimes, information security and related areas.

Chitra Ahanthem

Chitra Ahanthem is a features writer with Imphal Free Press, published in Imphal, Manipur. She is also a freelance writer and researcher on issues around HIV/AIDS, child rights, conflict and gender.

Baljit Singh Bedi

Baljit Singh Bedi did his B.Tech and M.Tech. from Indian Institute of Technology (IIT), Delhi. After serving for five years in the Centre for Applied Research in Electronics (CARE) IIT, Delhi he joined the Department of Information Technology (DIT), Ministry of Communication & IT (MCIT), Government of India. The major responsibilities and contributions over the years cover conceptualizing, evolving and implementation of a number of major schemes/programmes and projects in the field of electronics and IT applications with primary role in healthcare. He was instrumental in starting an integrated programme in promoting the area of Electronics, IT and Electronic Medical Records (EMR) Standards in Healthcare in India. As the head of Medical Electronics & Telemedicine division, he was looking after the activity of promotion of e-health & tele–health technology and R&D in medical electronics and launched a number of schemes in India. He was part of the National Task Force Telemedicine in India set up by the Ministry of Health & Family Welfare (MoH&FW), Government of India and headed the Group on Standards. He was a Member of National Knowledge Commission’s Working Group on India-Health Information Network Development (I-HIND) and is part of the Advisory Group for follow-up implementation program under the consideration of MoH&FW. He is actively involved in policy, development and deployment programmes of IT in Health initiatives of DIT, MoH&FW, and Media Lab Asia. He is a member of the National Committee set up by MoH&FW for EMR Standardization and Heading its Task Group on Interoperability. He is also International Telecommunication Union (ITU) Expert for e-Health Standardization. He is Executive Member of Indian Association of Medical Informatics (IAMI) and President, Telemedicine Society of India (TSI). At present, he is an Adviser to the Centre for Development of Advanced Computing (CDAC), Scientific Society of MCIT, Government of India.

Deepak Maheshwari

Deepak Maheshwari is Director – Corporate Affairs with Microsoft in India and responsible for interactions with the policymakers & regulators as well as with industry associations & the civil society organizations. An active participant and a keen observer of the interplay between technological innovation and socio-economic development, he has been closely associated with development & evolution of Information & Communication Technology policy, law & regulation for more than a decade and is often invited as a speaker and a contributor of articles & opinions in the media.

He has been active in several trade associations and served as committee chair & co-chair. He served for two consecutive terms as the elected secretary in the ISP Association of India and co-founded National Internet eXchange of India (NIXI) as well as the ITU-APT Foundation of India. He is also a member on the academic board of the IIM Ahmedabad- IDEA Telecom Centre of Excellence.

At times mistaken as a lawyer, he was actually awarded degree in engineering by one of India’s leading technical institute IT-BHU. His professional experience of more than 2 decades spans functional responsibilities across sales, marketing, operations and last but not the least, corporate affairs.

*Participants to be confirmed

Download the poster here
Download the agenda here (PDF, 755 KB)

VIDEOS

For more details visit https://cis-india.org/internet-governance/privacy-symposium

Alexa’s recording leak in US ‘echoes’ privacy issues here

Admin — 2018-05-30T00:49:26Z

Market analyst Sanjay Mehta (name changed) has been keeping his Amazon Echo smart speaker mostly unplugged since reports surfaced last week of the device’s voice assistant, Alexa, inadvertently recording and sending out conversations of a family in the US.

The article by Mugdha Variyar was published in the Economic Times on May 29, 2018. Sunil Abraham was quoted.

Digital rights activist Nikhil Pahwa keeps his Google Home smart speaker occasionally plugged out, citing the propensity of the device’s voice assistant to assume it is being queried even when it is not. In the Portland case involving Echo, Alexa had misinterpreted a family’s conversation to be a request to record and send the conversation to a person in the family’s contacts list.

In India, as internet consumers become comfortable using AI-powered voice assistants to play music, set tasks and seek information, they are also waking up to the fragility of data privacy, especially after the infamous Facebook-Cambridge Analytica episode. Indian laws, though, are yet to catch up with technology such as these, say privacy experts.

Globally too, governments are grappling with framing policy around data and privacy. That said, the European Union’s tough privacy laws on how companies can handle user data, introduced last week, are forcing companies to seek consent from customers globally to use their data.

According to Singapore-based market research firm Canalys, 108,000 units of Amazon Echo devices were shipped to sales channels in India in the first quarter of this year. As for Google Home, which was launched here in April, 25,000 devices have been shipped so far.

“It is always the company’s fault when such incidents (Alexa’s recording leak) happen. But if it does happen in India, it will also be the government’s fault since there is a big vacuum when it comes to protecting privacy in the digital age,” said Sunil Abraham, executive director of Centre for Internet and Society.

Abraham said a recording device in homes could open up the possibility of hacking or wiretapping. He, however, added that the Amazon incident would not necessarily create any panic. Amazon did not respond to specific queries about what steps it was taking to ensure such incidents do not occur again.

Google said it provides a Home user control through its activity control feature, ability to delete voice-recording history and control permissions to personal data on Gmail, as well as the option to mute the device.

Abraham cited the principles of data minimisation, that is, bare minimum collection of data, and minimal data retention policies with the user, as the main policy requirements, especially to prevent incidents such as the Alexa leak. “We are hopeful that the Srikrishna Committee will include this in the data privacy law,” he added.

While there needs to be a strong law, there also needs to be a strong citizen advocacy, where users take a company to court for privacy breach. Alexa users should also be sending queries to Amazon about what steps they are taking for privacy protection.

For more details visit https://cis-india.org/internet-governance/news/economic-times-may-29-mugdha-variyar-alexas-recording-leak-in-us-echoes-privacy-issues-here

Airtel defends Airtel Zero, calls it toll-free service

praskrishna — 2015-05-09T09:19:05Z

Releasing an official statement, Airtel said its vision is to have every Indian on the Internet while appmakers will decide how to pay for data.

With the net neutrality debate raging in the online world, Airtel's Airtel Zero platform has been in the eye of storm. Internet activists, celebrities and social media users have expressed their distrust of the platform and are campaigning for an open Internet. On Saturday, Airtel sought to play down the concerns with a statement from CEO Gopal Vittalon which was circulated to customers and also shared on the company's official Facebook page.

Stating that the company's vision is to have every Indian on the Internet, Mr. Vittal's compares Airtel Zero with a toll-free service. "We are simply taking the concept of toll-free voice to the world of data. As a result, it is for the application developers and their customers to decide how data charges will be paid for." Mr. Vittal assures all customers that Airtel Zero will be a paltform that is open to all application developers, content providers and Internet sites on an equal basis.

Airtel Zero offers a platform for application developers and others to pay to feature their products and sites. However, this has raised the issue of big companies being able to pay a higher price to be featured on the platform while fledgling companies might be at a disadvantage. This, according to many pro-net neutrality activists, violates the very essence of open Internet.

Read the full article published in the Hindu on April 20, 2015. Pranesh Prakash was quoted.

For more details visit https://cis-india.org/internet-governance/news/the-hindu-april-20-2015-airtel-defends-airtel-zero-calls-it-toll-free-service

AI: Full Spectrum Regulatory Challenge Launch Workshop [Reference Files]

pranav — 2020-08-04T06:08:48Z

These are the files released at the AI Full Spectrum Regulatory Challenge Launch Event, organised by CIS, and CCG-NLUD on September 27 2019. At the event, Sunil Abraham discussed the draft policy brief linked below, which is an output of the Regulatory Practices Lab at CIS.

The Event poster can be found here.

The Infographic in the Policy brief can be found here.

The working draft that was released at the workshop can be found here.

For more details visit https://cis-india.org/internet-governance/ai-full-spectrum-regulatory-challenge-launch-workshop-reference-files

AI Opera- AI as a total work of art

Admin — 2019-10-14T14:30:56Z

On October 11, 2019, Shweta Mohandas and Mira were invited as panelists for the 'AI Opera- AI as a total work of art' event organized by Goethe as part of the India Week Hamburg 2019 held in Bangalore. CIS was an event partner.

The panel had to present different perspectives and possibilities of Artificial Intelligence (AI). The discussion was facilitated by German artist, performer and filmmaker Christoph Faulhaber. For more info, click here.

For more details visit https://cis-india.org/internet-governance/news/ai-opera-ai-as-a-total-work-of-art

AI is biased, you’ll see if you Google ‘hands’

Rajmohan Sudhakar — 2019-08-26T23:53:38Z

As it is, the world is unfair. The question now is, do we want automated tech to be unfair too? As we build more and more AI-dependent smart digital infrastructure in our cities and beyond, we have pretty much overlooked the emerging character of artificial intelligence that would have a profound bearing on our nature and future.

The article by Rajmohan Sudhakar was published in Deccan Herald on August 25, 2019. Radhika Radhakrishnan was quoted.

Are we happy with algorithms making decisions for us? Naturally, one would expect the algorithm to possess discretion. Herein lies the dilemma. Do you trust an AI algorithm? Though an algorithm can evolve over time drawing on the nature and accuracy of the dataset, it shall nevertheless pick up the prejudices and biases it is exposed to.

“Questions on fairness arise at multiple stages of AI design. For instance, who has access to large datasets? The private sector in India. There may not be data at all on marginalised communities while there can be excessive surveillance data on targeted communities. Historic biases in datasets add up: widely used leading datasets of word embeddings associate women as homemakers and men as computer programmers. Focus on FAT (Fairness, Accountability and Transparency) is crucial,” says Radhika Radhakrishnan, programme officer at The Centrefor Internet and Society.

For example, a whopping 90% of the Wikipedia editors are men.

As AI is expected to add 15 trillion US dollars by 2030 to the global economy, at present, the data it relies upon comes from a few nations (45% from the US) while a major chunk of users are elsewhere. As it is vital to any social mechanism, diversity will be key if we are to reap the true benefits of AI. Or else, a non-diverse data set or a programmer crafting an algorithm could chart the most unpleasant course.

“Research recommends the inclusion of social scientists in AI design and ensuring they have decision-making power. The AI Now Institute, for instance. However, there is a dearth of social scientists working on AI. In India, we ignore the social impact of AI in favour of the purely technical solutions of computer scientists. Lack of women, gender-queers, and individuals from under-represented communities reflects poor diversity within the AI industry,” Radhakrishnan points out.

The G20 adopted AI Principles in June, which stressed “AI actors should respect the rule of law,human rights and democratic values, throughout the AI system life-cycle. These include freedom, dignity and autonomy, privacy and data protection, non-discrimination and equality,diversity, fairness, social justice, and internationally recognised labour rights.”

The UK recently set up the Centre for Data Ethics and Innovation. Canada and France are spearheading the International Panel on Artificial Intelligence (IPAI) on the sidelines of the G7summit. Meanwhile, India and France agreed on a slew of measures to advance cooperation ondigital tech. Of course, the EU’s General Data Protection Regulation (GDPR) is a promising start.

All of that is well and welcome. But what such efforts and international bodies could achieve in reality is to be seen as questions loom large over private corporations that own tech exercising clout, henceforth leaving AI vulnerable to manipulation.

“To achieve this, panel members will need to be protected from direct or indirect lobbying by companies, pressure groups and governments — especially by those who regard ethics as a brake on innovation. That also means that panel members will need to be chosen for their expertise, not for which organisation they represent,” reads an August 21 editorial in Nature journal on IPAI.

Whatever one may do to de-bias AI, much damage is done already. Try a google search for images of hands. How many black/brown hands do you see? There you go.

“Whose needs are being reflected in AI — those of the poor or those of the big tech looking to‘dump’ their products in an easily exploitable market? Instead of asking, what is the AI solution,we should be wondering, is an AI-based solution necessary in this case?” adds Radhakrishnan.

Where are the big tech located? In the United States. When a white male sitting in that country crafts an algorithm based on a bought dataset, for the benefit of an aboriginal community in the Amazon, something’s amiss.

“Engineers and data scientists who design algorithms are often far removed from the socioeconomic contexts of the people they are designing the tools for. So, they reproduce ideologies that are damaging. They end up reinforcing prejudices. Direct engagement is rare. Engineers should actively and carefully challenge their biases and assumptions by engaging meaningfully with communities to understand their histories and needs,” explains Radhakrishnan.

The march of AI cannot be stopped as more and more datasets get integrated. An ethical approach to computer science and engineering should begin from our institutions of excellence.

“Computer science and engineering disciplines at the undergraduate level teach AI as a purelytechnical subject, not as an interdisciplinary subject. Engineers should be trained in the socialimplications of the systems they design. Technology inevitably re􀁻ects its creators, consciousor not. Therefore, deeper attention to the social contexts of AI and the potential impact of suchsystems when applied to human populations should be incorporated to university curricula,”notes Radhakrishnan.

For more details visit https://cis-india.org/internet-governance/news/deccan-herald-rajmohan-sudhakar-august-25-2019-ai-is-biased-you-see-if-you-google-hands

AI in the Banking and Finance Industry in India

Saman Goudarzi, Elonnai Hickok and Amber Sinha — 2018-06-19T11:48:39Z

This is a draft report that seeks to map the present state of use of AI in the banking and financial sector in India.

This draft report was prepared by Saman Goudarzi, Elonnai Hickok and Amber Sinha. It was edited by Shyam Ponappa. Mapping was done by Shweta Mohandas. Pranav M Bidare, Sidharth Ray, and Aayush Rathi provided research assistance in preparing this report.

Executive Summary

In the last couple of years, the finance and banking sectors in India have increasingly deployed and implemented AI technologies. Such technologies are being implemented for front-end and back end processes – offering solutions for both financial and business management operations. At the moment, the AI landscape appears to be overwhelmingly populated by natural language processing and natural language generation technologies culminating in numerous chatbot initiatives by various banking and financial actors. Arguably more significant – but less documented – is the usage of said technologies for financial decision making on a variety of issues including, credit-scoring, transactions, wealth and risk management, and fraud detection. These trends are largely facilitated by technology service companies – both large-scale firms and startups – that either work with established banking and financial institutions to deploy AI technologies or develop and offer their own financial services directly to consumers.

This draft report seeks to map the present state of use of AI in the banking and financial sector in India. In doing so, it explores:

Uses: What is the present use of AI in banking and finance? What is the narrative and discourse around AI and banking/finance in India?
Actors: Who are the key stakeholders involved in the development, implementation and regulation of AI in the banking/finance sector?
Impact: What is the potential and existing impact of AI in the banking and finance sectors?
Regulation: What are the challenges faced in policy making around AI in the banking and finance sectors?

The draft report first offers an overview of the ways in which AI is being used in the sector. This is followed by an examination of existing challenges to the adoption of AI and the significant legal and ethical concerns that need to be considered in light of these trends. Lastly, the draft report draws attention to a number of key government actions and initiatives surrounding AI related to the banking and finance industry, discusses challenges to the adoption and implementation of AI and articulates recommendations towards addressing the same.

Download the draft report here

19th June Update: This case study has been modified to remove interview quotes, which are in the process of being confirmed. The link above is the latest draft of the report.

For more details visit https://cis-india.org/internet-governance/blog/ai-in-banking-and-finance

AI in India: A Policy Agenda

Amber Sinha, Elonnai Hickok and Arindrajit Basu — 2018-09-05T15:39:59Z

Click to download the file

Background

Over the last few months, the Centre for Internet and Society has been engaged in the mapping of use and impact of artificial intelligence in health, banking, manufacturing, and governance sectors in India through the development of a case study compendium.^{^[1]} Alongside this research, we are examining the impact of Industry 4.0 on jobs and employment and questions related to the future of work in India. We have also been a part of several global conversations on artificial intelligence and autonomous systems. The Centre for Internet and Society is part of the Partnership on Artificial Intelligence, a consortium which has representation from some of most important companies and civil society organisations involved in developments and research on artificial intelligence. We have contributed to the The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, and are also a part of a Big Data for Development Global Network, where we are undertaking research towards evolving ethical principles for use of computational techniques. The following are a set of recommendations we have arrived out of our research into artificial intelligence, particularly the sectoral case studies focussed on the development and use of artificial intelligence in India.

National AI Strategies: A Brief Global Overview

Artificial Intelligence is emerging as a central policy issue in several countries. In October 2016, the Obama White House released a report titled, “Preparing for the Future of Artificial Intelligence”^{^[2]} delving into a range of issues including application for public goods, regulation, economic impact, global security and fairness issues. The White House also released a companion document called the “National Artificial Intelligence Research and Development Strategic Plan”^{^[3]} which laid out a strategic plan for Federally-funded research and development in AI. These were the first of a series of policy documents released by the US towards the role of AI. The United Kingdom announced its 2020 national development strategy and issued a government report to accelerate the application of AI by government agencies while in 2018 the Department for Business, Energy, and Industrial Strategy released the Policy Paper - AI Sector Deal.^{^[4]} The Japanese government released it paper on Artificial Intelligence Technology Strategy in 2017.^{^[5]} The European Union launched "SPARC," the world’s largest civilian robotics R&D program, back in 2014.^{^[6]}

Over the last year and a half, Canada,^{^[7]} China,^{^[8]} the UAE,^{^[9]} Singapore,^{^[10]} South Korea^{^[11]}, and France^{^[12]} have announced national AI strategy documents while 24 member States in the EU have committed to develop national AI policies that reflect a “European” approach to AI ^{^[13]}. Other countries such as Mexico and Malaysia are in the process of evolving their national AI strategies. What this suggests is that AI is quickly emerging as central to national plans around the development of science and technology as well as economic and national security and development. There is also a focus on investments enabling AI innovation in critical national domains as a means of addressing key challenges facing nations. India has followed this trend and in 2018 the government published two AI roadmaps - the Report of Task Force on Artificial Intelligence by the AI Task Force constituted by the Ministry of Commerce and Industry^{^[14]} and the National Strategy for Artificial Intelligence by Niti Aayog.^{^[15]} Some of the key themes running across the National AI strategies globally are spelt out below.

Economic Impact of AI

A common thread that runs across the different national approaches to AI is the belief in the significant economic impact of AI, that it will likely increase productivity and create wealth. The British government estimated that AI could add $814 billion to the UK economy by 2035. The UAE report states that by 2031, AI will help boost the country’s GDP by 35 per cent, reduce government costs by 50 per cent. Similarly, China estimates that the core AI market will be worth 150 billion RMB ($25bn) by 2020, 400 billion RMB ($65bn) and one trillion RMB ($160bn) by 2030. The impact of adoption of AI and automation of labour and employment is also a key theme touched upon across the strategies. For instance, the White House Report of October 2016 states the US workforce is unprepared – and that a serious education programme, through online courses and in-house schemes, will be required.^{^[16]}

State Funding

Another key trend exhibited in all national strategies towards AI has been a commitment by the respective governments towards supporting research and development in AI. The French government has stated that it intends to invest €1.5 billion ($1.85 billion) in AI research in the period through to 2022. The British government’s recommendations, in late 2017, were followed swiftly by a promise in the autumn budget of new funds, including at least £75 million for AI. Similarly, the the Canadian government put together a $125-million ‘pan-Canadian AI strategy’ last year.

AI for Public Good

The use of AI for Public Good is a significant focus of most AI policies. The biggest justification for AI innovation as a legitimate objective of public policy is its promised impact towards improvement of people’s lives by helping to solve some of the world’s greatest challenges and inefficiencies, and emerge as a transformative technology, much like mobile computing. These public good uses of AI are emerging across sectors such as transportation, migration, law enforcement and justice system, education, and agriculture..

National Institutions leading AI research

Another important trend which was key to the implementation of national AI strategies is the creation or development of well-funded centres of excellence which would serve as drivers of research and development and leverage synergies with the private sector. The French Institute for Research in Computer Science and Automation (INRIA) plans to create a national AI research program with five industrial partners. In UK, The Alan Turing Institute is likely to emerge as the national institute for data science, and an AI Council would be set up to manage inter-sector initiatives and training. In Canada, Canadian Institute for Advanced Research (CIFAR) has been tasked with implementing their AI strategy. Countries like Japan has a less centralised structure with the creation of strategic council for AI technology’ to promote research and development in the field, and manage a number of key academic institutions, including NEDO and its national ICT (NICT) and science and tech (JST) agencies. These institutions are key to successful implementation of national agendas and policies around AI.

AI, Ethics and Regulation

Across the AI strategies — ethical dimensions and regulation of AI were highlighted as concerns that needed to be addressed. Algorithmic transparency and explainability, clarity on liability, accountability and oversight, bias and discrimination, and privacy are ethical and regulatory questions that have been raised. Employment and the future of work is another area of focus that has been identified by countries. For example, the US 2016 Report reflected on if existing regulation is adequate to address risk or if adaption is needed by examining the use of AI in automated vehicles. In the policy paper - AI Sector Deal - the UK proposes four grand challenges: AI and Data Economy, Future Mobility, Clean Growth, and Ageing Society. The Pan Canadian Artificial Intelligence Strategy focuses on developing global thought leadership on the economic, ethical, policy, and legal implications of advances in artificial intelligence.^{^[17]}

The above are important factors and trends to take into account and to different extents have been reflected in the two national roadmaps for AI. Without adequate institutional planning, there is a risk of national strategies being too monolithic in nature. Without sufficient supporting mechanisms in the form of national institutions which would drive the AI research and innovation, capacity building and re-skilling of workforce to adapt to changing technological trends, building regulatory capacity to address new and emerging issues which may disrupt traditional forms of regulation and finally, creation of an environment of monetary support both from the public and private sector it becomes difficult to implement a national strategy and actualize the potentials of AI . As stated above, there is also a need for identification of key national policy problems which can be addressed by the use of AI, and the creation of a framework with institutional actors to articulate the appropriate plan of action to address the problems using AI. There are several ongoing global initiatives which are in the process of trying to articulate key principles for ethical AI. These discussions also feature in some of the national strategy documents.

Key considerations for AI policymaking in India

As mentioned above, India has published two national AI strategies. We have responded to both of these here^{^[18]} and here.^{^[19]} Beyond these two roadmaps, this policy brief reflects on a number of factors that need to come together for India to leverage and adopt AI across sectors, communities, and technologies successfully.

Resources, Infrastructure, Markets, and Funding

Ensure adequate government funding and investment in R&D

As mentioned above, a survey of all major national strategies on AI reveals a significant financial commitment from governments towards research and development surrounding AI. Most strategy documents speak of the need to safeguard national ambitions in the race for AI development. In order to do so it is imperative to have a national strategy for AI research and development, identification of nodal agencies to enable the process, and creation of institutional capacity to carry out cutting edge research.

Most jurisdictions such as Japan, UK and China have discussed collaborations between the industry and government to ensure greater investment into AI research and development. The European Union has spoken using the existing public-private partnerships, particularly in robotics and big data to boost investment by over one and half times.^{^[20]} To some extent, this step has been initiated by the Niti Aayog strategy paper. The paper lists out enabling factors for the widespread adoption of AI and maps out specific government agencies and ministries that could promote such growth. In February 2018, the Ministry of Electronics and IT also set up four committees to prepare a roadmap for a national AI programme. The four committees are presently studying AI in context of citizen centric services; data platforms; skilling, reskilling and R&D; and legal, regulatory and cybersecurity perspectives.^{^[21]}

Democratize AI technologies and data

Clean, accurate, and appropriately curated data is essential for training algorithms. Importantly, large quantities of data alone does not translate into better results. Accuracy and curation of data should be prerequisites to quantity of data. Frameworks to generate and access larger quantity of data should not hinge on models of centralized data stores. The government and the private sector are generally gatekeepers to vast amounts of data and technologies. Ryan Calo has called this an issue of data parity,^{^[22]} where only a few well established leaders in the field have the ability to acquire data and build datasets. Gaining access to data comes with its own questions of ownership, privacy, security, accuracy, and completeness. There are a number of different approaches and techniques that can be adopted to enable access to data.

Open Government Data

Robust open data sets is one way in which access can be enabled. Open data is particularly important for small start-ups as they build prototypes. Even though India is a data dense country and has in place a National Data and Accessibility Policy India does not yet have robust and comprehensive open data sets across sectors and fields. Our research found that this is standing as an obstacle to innovation in the Indian context as startups often turn to open datasets in the US and Europe for developing prototypes. Yet, this is problematic because the demography represented in the data set is significantly different resulting in the development of solutions that are trained to a specific demographic, and thus need to be re-trained on Indian data. Although AI is technology agnostic, in the cases of different use cases of data analysis, demographically different training data is not ideal. This is particularly true for certain categories such as health, employment, and financial data.

The government can play a key role in providing access to datasets that will help the functioning and performance of AI technologies. The Indian government has already made a move towards accessible datasets through the Open Government Data Platform which provides access to a range of data collected by various ministries. Telangana has developed its own Open Data Policy which has stood out for its transparency and the quality of data collected and helps build AI based solutions.

In order to encourage and facilitate innovation, the central and state governments need to actively pursue and implement the National Data and Accessibility Policy.

Access to Private Sector Data

The private sector is the gatekeeper to large amounts of data. There is a need to explore different models of enabling access to private sector data while ensuring and protecting users rights and company IP. This data is often considered as a company asset and not shared with other stakeholders. Yet, this data is essential in enabling innovation in AI.

Amanda Levendowski states that ML practitioners have essentially three options in securing sufficient data— build the databases themselves, buy the data, or use data in the public domain. The first two alternatives are largely available to big firms or institutions. Smaller firms often end resorting to the third option but it carries greater risks of bias.

A solution could be federated access, with companies allowing access to researchers and developers to encrypted data without sharing the actual data. Another solution that has been proposed is ‘watermarking’ data sets.

Data sandboxes have been promoted as tools for enabling innovation while protecting privacy, security etc. Data sandboxes allow companies access to large anonymized data sets under controlled circumstances. A regulatory sandbox is a controlled environment with relaxed regulations that allow the product to be tested thoroughly before it is launched to the public. By providing certification and safe spaces for testing, the government will encourage innovation in this sphere. This system has already been adopted in Japan where there are AI specific regulatory sandboxes to drive society 5.0.160 data sandboxes are tools that can be considered within specific sectors to enable innovation. A sector wide data sandbox was also contemplated by TRAI.^{^[23]} A sector specific governance structure can establish a system of ethical reviews of underlying data used to feed the AI technology along with data collected in order to ensure that this data is complete, accurate and has integrity. A similar system has been developed by Statistics Norway and the Norwegian Centre for Research Data.^{^[24]}

AI Marketplaces

The National Roadmap for Artificial Intelligence by NITI Aayog proposes the creation of a National AI marketplace that is comprised of a data marketplace, data annotation marketplace, and deployable model marketplace/solutions marketplace.^{^[25]} In particular, it is envisioned that the data marketplace would be based on blockchain technology and have the features of: traceability, access controls, compliance with local and international regulations, and robust price discovery mechanism for data. Other questions that will need to be answered center around pricing and ensuring equal access. It will also be interesting how the government incentivises the provision of data by private sector companies. Most data marketplaces that are emerging are initiated by the private sector.^{^[26]} A government initiated marketplace has the potential to bring parity to some of the questions raised above, but it should be strictly limited to private sector data in order to not replace open government data.

Open Source Technology

A number of companies are now offering open source AI technologies. For example, TensorFlow, Keras, Scikit-learn, Microsoft Cognitive Toolkit, Theano, Caffe, Torch, and Accord.NET.^{^[27]} The government should incentivise and promote open source AI technologies towards harnessing and accelerating research in AI.

Re-thinking Intellectual Property Regimes

Going forward it will be important for the government to develop an intellectual property framework that encourages innovation. AI systems are trained by reading, viewing, and listening to copies of human-created works. These resources such as books, articles, photographs, films, videos, and audio recordings are all key subjects of copyright protection. Copyright law grants exclusive rights to copyright owners, including the right to reproduce their works in copies, and one who violates one of those exclusive rights “is an infringer of copyright.^{^[28]}

The enterprise of AI is, to this extent, designed to conflict with tenets of copyright law, and after the attempted ‘democratization’ of copyrighted content by the advent of the Internet, AI poses the latest challenge to copyright law. At the centre of this challenge is the fact that it remains an open question whether a copy made to train AI is a “copy” under copyright law, and consequently whether such a copy is an infringement.^{^[29]} The fractured jurisprudence on copyright law is likely to pose interesting legal questions with newer use cases of AI. For instance, Google has developed a technique called federated learning, popularly referred to as on-device ML, in which training data is localised to the originating mobile device rather than copying data to a centralized server.^{^[30]} The key copyright questions here is whether decentralized training data stored in random access memory (RAM) would be considered as “copies”.^{^[31]} There are also suggestions that copies made for the purpose of training of machine learning systems may be so trivial or de minimis that they may not qualify as infringement.^{^[32]} For any industry to flourish, there needs to be legal and regulatory clarity and it is imperative that these copyright questions emerging out of use of AI be addressed soon.

As noted in our response to the Niti Aayog national AI strategy “The report also blames the current Indian Intellectual Property regime for being “unattractive” and averse to incentivising research and adoption of AI. Section 3(k) of Patents Act exempts algorithms from being patented, and the Computer Related Inventions (CRI) Guidelines have faced much controversy over the patentability of mere software without a novel hardware component. The paper provides no concrete answers to the question of whether it should be permissible to patent algorithms, and if yes, to to what extent. Furthermore, there needs to be a standard either in the CRI Guidelines or the Patent Act, that distinguishes between AI algorithms and non-AI algorithms. Additionally, given that there is no historical precedence on the requirement of patent rights to incentivise creation of AI, innovative investment protection mechanisms that have lesser negative externalities, such as compensatory liability regimes would be more desirable. The report further failed to look at the issue holistically and recognize that facilitating rampant patenting can form a barrier to smaller companies from using or developing AI. This is important to be cognizant of given the central role of startups to the AI ecosystem in India and because it can work against the larger goal of inclusion articulated by the report.”^{^[33]}

National infrastructure to support domestic development

Building a robust national Artificial Intelligence solution requires establishing adequate indigenous infrastructural capacity for data storage and processing. While this should not necessarily extend to mandating data localisation as the draft privacy bill has done, capacity should be developed to store data sets generated by indigenous nodal points.

AI Data Storage

Capacity needs to increase as the volume of data that needs to be processed in India increases. This includes ensuring effective storage capacity, IOPS (Input/Output per second) and ability to process massive amounts of data.

AI Networking Infrastructure

Organizations will need to upgrade their networks in a bid to upgrade and optimize efficiencies of scale. Scalability must be undertaken on a high priority which will require a high-bandwidth, low latency and creative architecture, which requires appropriate last mile data curation enforcement.

Conceptualization and Implementation

Awareness, Education, and Reskilling

Encouraging AI research

This can be achieved by collaborations between the government and large companies to promote accessibility and encourage innovation through greater R&D spending. The Government of Karnataka, for instance, is collaborating with NASSCOM to set up a Centre of Excellence for Data Science and Artificial Intelligence (CoE-DS&AI) on a public-private partnership model to “accelerate the ecosystem in Karnataka by providing the impetus for the development of data science and artificial intelligence across the country.” Similar centres could be incubated in hospitals and medical colleges in India. Principles of public funded research such as FOSS, open standards, and open data should be core to government initiatives to encourage research. The Niti Aaayog report proposes a two tier integrated approach towards accelerating research, but is currently silent on these principles.^{^[34]}

Therefore,as suggested by the NITI AAYOG Report, the government needs to set up ‘centres of excellence’. Building upon the stakeholders identified in the NITI AAYOG Report, the centers of excellence should involve a wide range of experts including lawyers, political philosophers, software developers, sociologists and gender studies from diverse organizations including government, civil society,the private sector and research institutions to ensure the fair and efficient roll out of the technology.^{^[35]} An example is the Leverhulme Centre for the Future of Intelligence set up by the Leverhulme Foundation at the University of Cambridge^{^[36]} and the AI Now Institute at New York University (NYU)^{^[37]} These research centres bring together a wide range of experts from all over the globe.^{^[38]}

Skill sets to successfully adopt AI

Educational institutions should provide opportunities for students to skill themselves to adapt to adoption of AI, and also push for academic programmes around AI. It is also important to introduce computing technologies such as AI in medical schools in order to equip doctors to adopt the technical skill sets and ethics required to use integrate AI in their practices. Similarly, IT institutes could include courses on ethics, privacy, accountability etc. to equip engineers and developers with an understanding of the questions surrounding the technology and services they are developing.

Societal Awareness Building

Much of the discussion around skilling for AI is in the context of the workplace, but there is a need for awareness to be developed across society for a broader adaptation to AI. The Niti Aayog report takes the first steps towards this - noting the importance of highlighting the benefits of AI to the public. The conversation needs to go beyond this towards enabling individuals to recognize and adapt to changes that might be brought about - directly and indirectly - by AI - inside and outside of the workplace. This could include catalyzing a shift in mindset to life long learning and discussion around potential implications of human-machine interactions.

Early Childhood Awareness and Education

It is important that awareness around AI begins in early childhood. This is in part because children already interact with AI and increasingly will do so and thus awareness is needed in how AI works and can be safely and ethically used. It is also important to start building the skills that will be necessary in an AI driven society from a young age.

Focus on marginalised groups

Awareness, skills, and education should be targeted at national minorities including rural communities, the disabled, and women. Further, there should be a concerted focus on communities that are under-represented in the tech sector-such as women and sexual minorities-to ensure that the algorithms themselves and the community working on AI driven solutions are holistic and cohesive. For example, Iridescent focuses on girls, children, and families to enable them to adapt to changes like artificial intelligence through promoting curiosity, creativity, and perseverance to become lifelong learners.^{^[39]} This will be important towards ensuring that AI does not deepen societal and global inequalities including digital divides. Widespread use of AI will undoubtedly require re-skilling various stakeholders in order to make them aware of the prospects of AI.^{^[40]} Artificial Intelligence itself can be used as a resource in the re-skilling process itself-as it would be used in the education sector to gauge people’s comfort with the technology and plug necessary gaps.

Improved access to and awareness of Internet of Things

The development of smart content or Intelligent Tutoring Systems in the education can only be done on a large scale if both the teacher and the student has access to and feel comfortable with using basic IoT devices . A U.K. government report has suggested that any skilled workforce using AI should be a mix of those with a basic understanding responsible for implementation at the grassroots level , more informed users and specialists with advanced development and implementation skills.^{^[41]}The same logic applies to the agriculture sector, where the government is looking to develop smart weather-pattern tracking applications. A potential short-term solution may lie in ensuring that key actors have access to an IoT device so that he/she may access digital and then impart the benefits of access to proximate individuals. In the education sector, this would involve ensuring that all teachers have access to and are competent in using an IoT device. In the agricultural sector, this may involve equipping each village with a set of IoT devices so that the information can be shared among concerned individuals. Such an approach recognizes that AI is not the only technology catalyzing change - for example industry 4.0 is understood as comprising of a suite of technologies including but not limited to AI.

Public Discourse

As solutions bring together and process vast amounts of granular data, this data can be from a variety of public and private sources - from third party sources or generated by the AI and its interaction with its environment. This means that very granular and non traditional data points are now going into decision making processes. Public discussion is needed to understand social and cultural norms and standards and how these might translate into acceptable use norms for data in various sectors.

Coordination and collaboration across stakeholders

Development of Contextually Nuanced and Appropriate AI Solutions

Towards ensuring effectiveness and accuracy it is important that solutions used in India are developed to account for cultural nuances and diversity. From our research this could be done in a number of ways ranging from: training AI solutions used in health on data from Indian patients to account for differences in demographics^{^[42]}, focussing on natural language voice recognition to account for the diversity in languages and digital skills in the Indian context,^{^[43]} and developing and applying AI to reflect societal norms and understandings.^{^[44]}

Continuing, deepening, and expanding partnerships for innovation

Continued innovation while holistically accounting for the challenges that AI poses will be key for actors in the different sectors to remain competitive. As noted across case study reports partnerships is key in facilitating this innovation and filling capacity gaps. These partnerships can be across sectors, institutions, domains, geographies, and stakeholder groups. For example: finance/ telecom, public/private, national/international, ethics/software development/law, and academia/civil society/industry/government. We would emphasize collaboration between actors across different domains and stakeholder groups as developing holistics AI solutions demands multiple understandings and perspectives.

Coordinated Implementation

Key sectors in India need to begin to take steps to consider sector wide coordination in implementing AI. Potential stress and system wide vulnerabilities would need to be considered when undertaking this. Sectoral regulators such as RBI, TRAI, and the Medical Council of India are ideally placed to lead this coordination.

Develop contextual standard benchmarks to assess quality of algorithms

In part because of the nacency of the development and implementation of AI, towards enabling effective assessments of algorithms to understand impact and informing selection by institutions adopting solutions, standard benchmarks can help in assessing quality and appropriateness of algorithms. It may be most effective to define such benchmarks at a sectoral level (finance etc.) or by technology and solution (facial recognition etc.). Ideally, these efforts would be led by the government in collaboration with multiple stakeholders.

Developing a framework for working with the private sector for use-cases by the government

There are various potential use cases the government could adopt in order to use AI as a tool for augmenting public service delivery in India by the government. However, given lack of capacity -both human resource and technological-means that entering into partnerships with the private sector may enable more fruitful harnessing of AI- as has been seen with existing MOUs in the agricultural^{^[45]} and healthcare sectors.^{^[46]} However, the partnership must be used as a means to build capacity within the various nodes in the set-up rather than relying only on the private sector partner to continue delivering sustainable solutions.

Particularly, in the case of use of AI for governance, there is a need to evolve a clear parameter to do impact assessment prior to the deployment of the technology that clearly tries to map estimated impact of the technology of clearly defined objectives, which must also include the due process, procedural fairness and human rights considerations . As per Article 12 of the Indian Constitution, whenever the government is exercising a public function, it is bound by the entire gamut of fundamental rights articulated in Part III of the Constitution. This is a crucial consideration the government will have to bear in mind whenever it uses AI-regardless of the sector. In all cases of public service delivery, primary accountability for the use of AI should lie with the government itself, which means that a cohesive and uniform framework which regulates these partnerships must be conceptualised. This framework should incorporate : (a) Uniformity in the wording and content of contracts that the government signs, (b) Imposition of obligations of transparency and accountability on the developer to ensure that the solutions developed are in conjunction with constitutional standards and (c) Continuous evaluation of private sector developers by the government and experts to ensure that they are complying with their obligations.

Defining Safety Critical AI

The implications of AI differs according to use. Some countries, such as the EU, are beginning to define sectors where AI should play the role of augmenting jobs as opposed to functioning autonomously. The Global Partnership on AI is has termed sectors where AI tools supplement or replace human decision making in areas such as health and transportation as ‘safety critical AI’ and is researching best practices for application of AI in these areas. India will need to think through if there is a threshold that needs to be set and more stringent regulation applied. In addition to uses in health and transportation, defense and law enforcement would be another sector where certain use would require more stringent regulation.

Appropriate certification mechanisms

Appropriate certificate mechanisms will be important in ensuring the quality of AI solutions. A significant barrier to the adoption of AI in some sectors in India is acceptability of results, which include direct results arrived at using AI technologies as well as opinions provided by practitioners that are influenced/aided by AI technologies. For instance, start-ups in the healthcare sectors often find that they are asked to show proof of a clinical trial when presenting their products to doctors and hospitals, yet clinical trials are expensive, time consuming and inappropriate forms of certification for medical devices and digital health platforms. Startups also face difficulty in conducting clinical trials as there is lack of a clear regulation to adhere to. They believe that while clinical trials are a necessity with respect to drugs, the process often results in obsolescence of the technology by the time it is approved in the context of AI. Yet, medical practitioners are less trusting towards startups who do not have approval from a national or international authority. A possible and partial solution suggested by these startups is to enable doctors to partner with them to conduct clinical trials together. However, such partnerships cannot be at the expense of rigour, and adequate protections need to be built in the enabling regulation.

Serving as a voice for emerging economies in the global debate on AI

While India should utilise Artificial Intelligence in the economy as a means of occupying a driving role in the global debate around AI, it must be cautious before allowing the use of Indian territory and infrastructure as a test bed for other emerging economies without considering the ramifications that the utilisation of AI may have for Indian citizens. The NITI AAYOG Report envisions India as leverage AI as a ‘garage’ for emerging economies.^{^[47]} While there are certain positive connotations of this suggestion in so far as this propels India to occupy a leadership position-both technically and normatively in determining future use cases for AI in India,, in order to ensure that Indian citizens are not used as test subjects in this process, guiding principles could be developed such as requiring that projects have clear benefits for India.

Frameworks for Regulation

National legislation

Data Protection Law

India is a data-dense country, and the lack of a robust privacy regime, allows the public and private sector easier access to large amounts of data than might be found in other contexts with stringent privacy laws. India also lacks a formal regulatory regime around anonymization. In our research we found that this gap does not always translate into a gap in practice, as some start up companies have adopted self-regulatory practices towards protecting privacy such as of anonymising data they receive before using it further, but it does result in unclear and unharmonized practice..

In order to ensure rights and address emerging challenges to the same posed by artificial intelligence, India needs to enact a comprehensive privacy legislation applicable to the private and public sector to regulate the use of data, including use in artificial intelligence. A privacy legislation will also have to address more complicated questions such as the use of publicly available data for training algorithms, how traditional data categories (PI vs. SPDI - meta data vs. content data etc.) need to be revisited in light of AI, and how can a privacy legislation be applied to autonomous decision making. Similarly, surveillance laws may need to be revisited in light of AI driven technologies such as facial recognition, UAS, and self driving cars as they provide new means of surveillance to the state and have potential implications for other rights such as the right to freedom of expression and the right to assembly. Sectoral protections can compliment and build upon the baseline protections articulated in a national privacy legislation.^{^[48]} In August 2018 the Srikrishna Committee released a draft data protection bill for India. We have reflected on how the Bill addresses AI. Though the Bill brings under its scope companies deploying emerging technologies and subjects them to the principles of privacy by design and data impact assessments, the Bill is silent on key rights and responsibilities, namely the responsibility of the data controller to explain the logic and impact of automated decision making including profiling to data subjects and the right to opt out of automated decision making in defined circumstances.^{^[49]} Further, the development of technological solutions to address the dilemma between AI and the need for access to larger quantities of data for multiple purposes and privacy should be emphasized.

Discrimination Law

A growing area of research globally is the social consequences of AI with a particular focus on its tendency to replicate or amplify existing and structural inequalities. Problems such as data invisibility of certain excluded groups,^{^[50]} the myth of data objectivity and neutrality,^{^[51]} and data monopolization^{^[52]} contribute to the disparate impacts of big data and AI. So far much of the research on this subject has not moved beyond the exploratory phase as is reflected in the reports released by the White House^{^[53]} and Federal Trade Commission^{^[54]} in the United States. The biggest challenge in addressing discriminatory and disparate impacts of AI is ascertaining “where value-added personalization and segmentation ends and where harmful discrimination begins.”^{^[55]}

Some prominent cases where AI can have discriminatory impact are denial of loans based on attributes such as neighbourhood of residence as a proxies which can be used to circumvent anti-discrimination laws which prevent adverse determination on the grounds of race, religion, caste or gender, or adverse findings by predictive policing against persons who are unfavorably represented in the structurally biased datasets used by the law enforcement agencies. There is a dire need for disparate impact regulation in sectors which see the emerging use of AI.

Similar to disparate impact regulation, developments in AI, and its utilisation, especially in credit rating, or risk assessment processes could create complex problems that cannot be solved only by the principle based regulation. Instead, regulation intended specifically to avoid outcomes that the regulators feel are completely against the consumer, could be an additional tool that increases the fairness, and effectiveness of the system.

Competition Law

The conversation of use of competition or antitrust laws to govern AI is still at an early stage. However, the emergence of numerous data driven mergers or acquisitions such as Yahoo-Verizon, Microsoft-LinkedIn and Facebook-WhatsApp have made it difficult to ignore the potential role of competition law in the governance of data collection and processing practices. It is important to note that the impact of Big Data goes far beyond digital markets and the mergers of companies such as Bayer, Climate Corp and Monsanto shows that data driven business models can also lead to the convergence of companies from completely different sectors as well. So far, courts in Europe have looked at questions such as the impact of combination of databases on competition^{^[56]} and have held that in the context of merger control, data can be a relevant question if an undertaking achieves a dominant position through a merger, making it capable of gaining further market power through increased amounts of customer data. The evaluation of the market advantages of specific datasets has already been done in the past, and factors which have been deemed to be relevant have included whether the dataset could be replicated under reasonable conditions by competitors and whether the use of the dataset was likely to result in a significant competitive advantage.^{^[57]} However, there are limited circumstances in which big data meets the four traditional criteria for being a barrier to entry or a source of sustainable competitive advantage — inimitability, rarity, value, and non-substitutability.^{^[58]}

Any use of competition law to curb data-exclusionary or data-exploitative practices will first have to meet the threshold of establishing capacity for a firm to derive market power from its ability to sustain datasets unavailable to its competitors. In this context the peculiar ways in which network effects, multi-homing practices and how dynamic the digital markets are, are all relevant factors which could have both positive and negative impacts on competition. There is a need for greater discussion on data as a sources of market power in both digital and non-digital markets, and how this legal position can used to curb data monopolies, especially in light of government backed monopolies for identity verification and payments in India.

Consumer Protection Law

The Consumer Protection Bill, 2015, tabled in the Parliament towards the end of the monsoon session has introduced an expansive definition of the term “unfair trade practices.” The definition as per the Bill includes the disclosure “to any other person any personal information given in confidence by the consumer.” This clause excludes from the scope of unfair trade practices, disclosures under provisions of any law in force or in public interest. This provision could have significant impact on the personal data protection law in India. Alongside, there is also a need to ensure that principles such as safeguarding consumers personal information in order to ensure that the same is not used to their detriment are included within the definition of unfair trade practices. This would provide consumers an efficient and relatively speedy forum to contest adverse impacts on them of data driven decision-making.

Sectoral Regulation

Our research into sectoral case studies revealed that there are a number of existing sectoral laws and policies that are applicable to aspects of AI. For example, in the health sector there is the Medical Council Professional Conduct, Etiquette, and Ethics Regulations 2002, the Electronic Health Records Standards 2016, the draft Medical Devices Rules 2017, the draft Digital Information Security in Healthcare Act. In the finance sector there is the Credit Information Companies (Regulation) Act 2005 and 2006, the Securities and Exchange Board of India (Investment Advisers) Regulations, 2013, the Payment and Settlement Systems Act, 2007, the Banking Regulations Act 1949, SEBI guidelines on robo advisors etc. Before new regulations, guidelines etc are developed - a comprehensive exercise needs to be undertaken at a sectoral level to understand if 1. sectoral policy adequately addresses the changes being brought about by AI 2. If it does not - is an amendment possible and if not - what form of policy would fill the gap.

Principled approach

Transparency

Audits

Internal and external audits can be mechanisms towards creating transparency about the processes and results of AI solutions as they are implemented in a specific context. Audits can take place while a solution is still in ‘pilot’ mode and on a regular basis during implementation. For example, in the Payment Card Industry (PCI) tool, transparency is achieved through frequent audits, the results of which are simultaneously and instantly transmitted to the regulator and the developer. Ideally parts of the results of the audit are also made available to the public, even if the entire results are not shared.

Tiered Levels of Transparency

There are different levels and forms of transparency as well as different ways of achieving the same. The type and form of transparency can be tiered and dependent on factors such as criticality of function, potential direct and indirect harm, sensitivity of data involved, actor using the solution . The audience can also be tiered and could range from an individual user to senior level positions, to oversight bodies.

Human Facing Transparency

It will be important for India to define standards around human-machine interaction including the level of transparency that will be required. Will chatbots need to disclose that they are chatbots? Will a notice need to be posted that facial recognition technology is used in a CCTV camera? Will a company need to disclose in terms of service and privacy policies that data is processed via an AI driven solution? Will there be a distinction if the AI takes the decision autonomously vs. if the AI played an augmenting role? Presently, the Niti Aayog paper has been silent on this question.

Explainability

An explanation is not equivalent to complete transparency. The obligation of providing an explanation does not mean that the developer should necessarily know the flow of bits through the AI system. Instead, the legal requirement of providing an explanation requires an ability to explain how certain parameters may be utilised to arrive at an outcome in a certain situation.

Doshi-Velez and Kortz have highlighted two technical ideas that may enhance a developer's ability to explain the functioning of AI systems:^{^[59]}

1) Differentiation and processing: AI systems are designed to have the inputs differentiated and processed through various forms of computation-in a reproducible and robust manner. Therefore, developers should be able to explain a particular decision by examining the inputs in an attempt to determine which of them have the greatest impact on the outcome.

2) Counterfactual faithfulness: The second property of counterfactual faithfulness enables the developer to consider which factors caused a difference in the outcomes. Both these solutions can be deployed without necessarily knowing the contents of black boxes. As per Pasquale, ‘Explainability matters because the process of reason-giving is intrinsic to juridical determinations – not simply one modular characteristic jettisoned as anachronistic once automated prediction is sufficiently advanced.”^{^[60]}

Rules based system applied contextually

Oswald et al have suggested two proposals that might mitigate algorithmic opacity.by designing a broad rules-based system, whose implementation need to be applied in a context-specific manner which thoroughly evaluates the key enablers and challengers in each specific use case.^{^[61]}

Experimental proportionality was designed to enable the courts to make proportionality determinations of an algorithm at the experimental stage even before the impacts are fully realised in a manner that would enable them to ensure that appropriate metrics for performance evaluation and cohesive principles of design have been adopted. In such cases they recommend that the courts give the benefit of the doubt to the public sector body subject to another hearing within a stipulated period of time once data on the impacts of the algorithm become more readily available.
‘ALGO-CARE' calls for the design of a rules-based system which ensures that the algorithms^{^[62]} are:

(1) Advisory: Algorithms must retain an advisory capacity that augments existing human capability rather than replacing human discretion outright;

(2) Lawful: Algorithm's proposed function, application, individual effect and use of datasets should be considered in symbiosis with necessity, proportionality and data minimisation principles;

(3) Granularity: Issues such as data analysis issues such as meaning of data, challenges stemming from disparate tracts of data, omitted data and inferences should be key points in the implementation process;

(4) Ownership: Due regard should be given to intellectual property ownership but in the case of algorithms used for governance, it may be better to have open source algorithms at the default. Regardless of the sector,the developer must ensure that the algorithm works in a manner that enables a third party to investigate the workings of the algorithm in an adversarial judicial context.

(5)Challengeable:The results of algorithmic analysis should be applied with regard to professional codes and regulations and be challengeable. In a report evaluating the NITI AAYOG Discussion Paper, CIS has argued that AI that is used for governance , must be made auditable in the public domain,if not under Free and Open Source Software (FOSS)-particularly in the case of AI that has implications for fundamental rights.^{^[63]}

(6) Accuracy: The design of the algorithm should check for accuracy;

(7) Responsible: Should consider a wider set of ethical and moral principles and the foundations of human rights as a guarantor of human dignity at all levels and

(8) Explainable: Machine Learning should be interpretable and accountable.

A rules based system like ALGO-CARE can enable predictability in use frameworks for AI. Predictability compliments and strengthens transparency.

Accountability

Conduct Impact Assessment

There is a need to evolve Algorithmic Impact Assessment frameworks for the different sectors in India, which should address issues of bias, unfairness and other harmful impacts of use of automated decision making. AI is a nascent field and the impact of the technology on the economy, society, etc. is still yet to be fully understood. Impact assessment standards will be important in identifying and addressing potential or existing harms and could potentially be more important in sectors or uses where there is direct human interaction with AI or power dimensions - such as in healthcare or use by the government. A 2018 Report by the AI Now Institute lists methods that should be adopted by the government for conducting his holistic assessment^{^[64]}: These should include: (1) Self-assessment by the government department in charge of implementing the technology, (2)Development of meaningful inter-disciplinary external researcher review mechanisms, (3) Notice to the public regarding self-assessment and external review, (4)Soliciting of public comments for clarification or concerns, (5) Special regard to vulnerable communities who may not be able to exercise their voice in public proceedings. An adequate review mechanism which holistically evaluates the impact of AI would ideally include all five of these components in conjunction with each other.

Regulation of Algorithms

Experts have voiced concerns about AI mimicking human prejudices due to the biases present in the Machine Learning algorithms. Scientists have revealed through their research that machine learning algorithms can imbibe gender and racial prejudices which are ingrained in language patterns or data collection processes. Since AI and machine algorithms are data driven, they arrive at results and solutions based on available
and historical data. When this data itself is biased, the solutions presented by the AI will also be biased. While this is inherently discriminatory, scientists have provided solutions to rectify these biases which can occur at various stages by introducing a counter bias at another stage. It has also been suggested that data samples should be shaped in such a manner so as to minimise the chances of algorithmic bias. Ideally regulation of algorithms could be tailored - explainability, traceability, scrutability. We recommend that the national strategy on AI policy must take these factors into account and combination of a central agency driving the agenda, and sectoral actors framing regulations around specific uses of AI that are problematic and implementation is required.

As the government begins to adopt AI into governance - the extent to which and the circumstances autonomous decision making capabilities can be delegated to AI need to be questioned. Questions on whether AI should be autonomous, should always have a human in the loop, and should have a ‘kill-switch’ when used in such contexts also need to be answered. A framework or high level principles can help to guide these determinations. For example:

Modeling Human Behaviour: An AI solution trying to model human behaviour, as in the case of judicial decision-making or predictive policing may need to be more regulated, adhere to stricter standards, and need more oversight than an algorithm that is trying to predict ‘natural’ phenomenon such as traffic congestion or weather patterns.
Human Impact: An AI solution which could cause greater harm if applied erroneously-such as a robot soldier that mistakenly targets a civilian requires a different level and framework of regulation than an AI solution designed to create a learning path for a student in the education sector and errs in making an appropriate assessment..
Primary User: AI solutions whose primary users are state agents attempting to discharge duties in the public interest such as policemen, should be approached with more caution than those used by individuals such as farmers getting weather alerts

Fairness

It is possible to incorporate broad definitions of fairness into a wide range of data analysis and classification systems.^{^[65]} While there can be no bright-line rules that will necessarily enable the operator or designer of a Machine Learning System to arrive at an ex ante determination of fairness, from a public policy perspective, there must be a set of rules or best practices that explain how notions of fairness should be utilised in the real world applications of AI-driven solutions.^{^[66]} While broad parameters should be encoded by the developer to ensure compliance with constitutional standards, it is also crucial that the functioning of the algorithm allows for an ex-post determination of fairness by an independent oversight body if the impact of the AI driven solution is challenged.

Further, while there is no precedent on this anywhere in the world, India could consider establishing a Committee entrusted with the specific task of continuously evaluating the operation of AI-driven algorithms. Questions that the government would need to answer with regard to this body include:

What should the composition of the body be?
What should be the procedural mechanisms that govern the operation of the body?
When should the review committee step in? This is crucial because excessive review may re-entrench the bureaucracy that the AI driven solution was looking to eliminate.
What information will be necessary for the review committee to carry out its determination? Will there be conflicts with IP, and if so how will these be resolved?
To what degree will the findings of the committee be made public?
What powers will the committee have? Beyond making determinations, how will these be enforced?

Market incentives

Standards as a means to address data issues

With digitisation of legacy records and the ability to capture more granular data digitally, one of the biggest challenges facing Big Data is a lack of standardised data and interoperability frameworks. This is particularly true in the healthcare and medicine sector where medical records do not follow a clear standard, which poses a challenge to their datafication and analysis. The presence of developed standards in data management and exchange, interoperable Distributed Application Platform and Services, Semantic related standards for markup, structure, query, semantics, Information access and exchange have been spoken of as essential to address the issues of lack of standards in Big Data.^{^[67]}

Towards enabling usability of data, it is important that clear data standards are established. This has been recognized by Niti Aayog in its National Strategy for AI. On one hand, there can operational issues with allowing each organisation to choose their own specific standards to operate under, while on the other hand, non-uniform digitisation of data will also cause several practical problems, most primarily to do with interoperability of the individual services, as well as their usability. For instance, in the healthcare sector, though India has adopted an EHR policy, implementation of this policy is not yet harmonized - leading to different interpretations of ‘digitizing records (i.e taking snapshots of doctor notes), retention methods and periods, and comprehensive implementation across all hospital data. Similarly, while independent banks and other financial organisations are already following, or in the process of developing internal practices,there exist no uniform standards for digitisation of financial data. As AI development, and application becomes more mainstream in the financial sector, the lack of a fixed standard could create significant problems.

Better Design Principles in Data Collection

An enduring criticism of the existing notice and consent framework has been that long, verbose and unintelligible privacy notices are not efficient in informing individuals and helping them make rational choices. While this problem predates Big Data, it has only become more pronounced in recent times, given the ubiquity of data collection and implicit ways in which data is being collected and harvested. Further, constrained interfaces on mobile devices, wearables, and smart home devices connected in an Internet of Things amplify the usability issues of the privacy notices. Some of the issues with privacy notices include Notice complexity, lack of real choices, notices decoupled from the system collecting data etc. An industry standard for a design approach to privacy notices which includes looking at factors such as the timing of the notice, the channels used for communicating the notices, the modality (written, audio, machine readable, visual) of the notice and whether the notice only provides information or also include choices within its framework, would be of great help. Further, use of privacy by design principles can be done not just at the level of privacy notices but at each step of the information flow, and the architecture of the system can be geared towards more privacy enhanced choices.

^{^[1]} https://cis-india.org/internet-governance/blog/artificial-intelligence-in-india-a-compendium

^{^[2]} https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf

^{^[3]} https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf

^{^[4]} https://www.gov.uk/government/publications/artificial-intelligence-sector-deal/ai-sector-deal

^{^[5]} http://www.nedo.go.jp/content/100865202.pdf

^{^[6]} https://www.eu-robotics.net/sparc/10-success-stories/european-robotics-creating-new-markets.html?changelang=2

^{^[7]} https://www.cifar.ca/ai/pan-canadian-artificial-intelligence-strategy

^{^[8]} https://www.newamerica.org/cybersecurity-initiative/blog/chinas-plan-lead-ai-purpose-prospects-and-problems/

^{^[9]} http://www.uaeai.ae/en/

^{^[10]} https://www.aisingapore.org/

^{^[11]} https://news.joins.com/article/22625271

^{^[12]} https://www.aiforhumanity.fr/pdfs/MissionVillani_Report_ENG-VF.pdf

^{^[13]} https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe https://www.euractiv.com/section/digital/news/twenty-four-eu-countries-sign-artificial-intelligence-pact-in-bid-to-compete-with-us-china/

^{^[14]} https://www.aitf.org.in/

^{^[15]} http://www.niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AI-Discussion-Paper.pdf

^{^[16]} https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf

^{^[17]} https://www.cifar.ca/ai/pan-canadian-artificial-intelligence-strategy

^{^[18]} https://cis-india.org/internet-governance/blog/the-ai-task-force-report-the-first-steps-towards-indias-ai-framework

^{^[19]} https://cis-india.org/internet-governance/blog/niti-aayog-discussion-paper-an-aspirational-step-towards-india2019s-ai-policy

^{^[20]} https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe

^{^[21]} http://pib.nic.in/newsite/PrintRelease.aspx?relid=181007

^{^[22]} Ryan Calo, 2017 Artificial Intelligence Policy: A Primer and Roadmap. U.C. Davis L. Review,

Vol. 51, pp. 398 - 435.

^{^[23]} https://trai.gov.in/sites/default/files/CIS_07_11_2017.pdf

^{^[24]} https://www.datatilsynet.no/globalassets/global/english/ai-and-privacy.pdf

^{^[25]} http://www.niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AI-Discussion-Paper.pdf

^{^[26]} https://martechtoday.com/bottos-launches-a-marketplace-for-data-to-train-ai-models-214265

^{^[27]} https://opensource.com/article/18/5/top-8-open-source-ai-technologies-machine-learning

^{^[28]} Amanda Levendowski, How Copyright Law Can Fix Artificial Intelligence’s

Implicit Bias Problem, 93 WASH. L. REV. (forthcoming 2018) (manuscript at 23, 27-32),

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3024938.

^{^[29]} Id.

^{^[30]} H. Brendan McMahan, et al., Communication-Efficient Learning of Deep Networks

from Decentralized Data, arXiv:1602.05629 (Feb. 17, 2016), https://arxiv.org/abs/1602.05629.

^{^[31]} Id.

^{^[32]} Pierre N. Leval, Nimmer Lecture: Fair Use Rescued, 44 UCLA L. REV. 1449, 1457 (1997).

^{^[33]} https://cis-india.org/internet-governance/blog/niti-aayog-discussion-paper-an-aspirational-step-towards-india2019s-ai-policy

^{^[34]} https://cis-india.org/internet-governance/blog/niti-aayog-discussion-paper-an-aspirational-step-towards-india2019s-ai-policy

^{^[35]} Discussion Paper on National Strategy for Artificial Intelligence | NITI Aayog | National Institution for Transforming India. (n.d.) p. 54. Retrieved from http://niti.gov.in/content/national-strategy-ai-discussion-paper.

^{^[36]} Leverhulme Centre for the Future of Intelligence, http://lcfi.ac.uk/.

^{^[37]} AI Now, https://ainowinstitute.org/.

^{^[38]} https://cis-india.org/internet-governance/ai-and-governance-case-study-pdf

^{^[39]} http://iridescentlearning.org/

^{^[40]} https://cis-india.org/internet-governance/ai-and-governance-case-study-pdf

^{^[41]} Points, L., & Potton, E. (2017). Artificial intelligence and automation in the UK.

^{^[42]} Paul, Y., Hickok, E., Sinha, A. and Tiwari, U., Artificial Intelligence in the Healthcare Industry in India, Centre for Internet and Society. Available at https://cis-india.org/internet-governance/files/ai-and-healtchare-report.

^{^[43]} Goudarzi, S., Hickok, E., and Sinha, A., AI in the Banking and Finance Industry in India, Centre for Internet and Society. Available at https://cis-india.org/internet-governance/blog/ai-in-banking-and-finance.

^{^[44]} Paul, Y., Hickok, E., Sinha, A. and Tiwari, U., Artificial Intelligence in the Healthcare Industry in India, Centre for Internet and Society. Available at https://cis-india.org/internet-governance/files/ai-and-healtchare-report.

^{^[45]} https://news.microsoft.com/en-in/government-karnataka-inks-mou-microsoft-use-ai-digital-agriculture/

^{^[46]} https://news.microsoft.com/en-in/government-telangana-adopts-microsoft-cloud-becomes-first-state-use-artificial-intelligence-eye-care-screening-children/

^{^[47]} NITI Aayog. (2018). Discussion Paper on National Strategy for Artificial Intelligence. Retrieved from http://niti.gov.in/content/national-strategy-ai-discussion-paper. 18

^{^[48]} https://edps.europa.eu/sites/edp/files/publication/16-10-19_marrakesh_ai_paper_en.pdf

^{^[49]} https://cis-india.org/internet-governance/blog/the-srikrishna-committee-data-protection-bill-and-artificial-intelligence-in-india

^{^[50]} J. Schradie, The Digital Production Gap: The Digital Divide and Web 2.0 Collide. Elsevier Poetics, 39 (1).

^{^[51]} D Lazer, et al., The Parable of Google Flu: Traps in Big Data Analysis. Science. 343 (1).

^{^[52]} Danah Boyd and Kate Crawford, Critical Questions for Big Data. Information, Communication & Society. 15 (5).

^{^[53]} John Podesta, (2014) Big Data: Seizing Opportunities, Preserving Values, available at

http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf

^{^[54]} E. Ramirez, (2014) FTC to Examine Effects of Big Data on Low Income and Underserved Consumers at September Workshop, available at http://www.ftc.gov/news-events/press-releases/2014/04/ftc-examine-effects-big-data-lowincome-underserved-consumers

^{^[55]} M. Schrage, Big Data’s Dangerous New Era of Discrimination, available at http://blogs.hbr.org/2014/01/bigdatas-dangerous-new-era-of-discrimination/.

^{^[56]} Google/DoubleClick Merger case

^{^[57]} French Competition Authority, Opinion n°10-A-13 of 1406.2010,

http://www.autoritedelaconcurrence.fr/pdf/avis/10a13.pdf. That opinion of the Authority aimed at

giving general guidance on that subject. It did not focus on any particular market or industry

although it described a possible application of its analysis to the telecom industry.

^{^[58]} http://www.analysisgroup.com/is-big-data-a-true-source-of-market-power/#sthash.5ZHmrD1m.dpuf

^{^[59]} Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S., O'Brien, D., ... & Wood, A. (2017). Accountability of AI under the law: The role of explanation. arXiv preprint arXiv:1711.01134.

^{^[60]} Frank A. Pasquale ‘Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society’ (July 14, 2017). Ohio State Law Journal, Vol. 78, 2017; U of Maryland Legal Studies Research Paper No. 2017-21, 7.

^{^[61]} Oswald, M., Grace, J., Urwin, S., & Barnes, G. C. (2018). Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘Experimental’ proportionality. Information & Communications Technology Law, 27(2), 223-250.

^{^[62]} Ibid.

^{^[63]} Abraham S., Hickok E., Sinha A., Barooah S., Mohandas S., Bidare P. M., Dasgupta S., Ramachandran V., and Kumar S., NITI Aayog Discussion Paper: An aspirational step towards India’s AI policy. Retrieved from https://cis-india.org/internet-governance/files/niti-aayog-discussion-paper.

^{^[64]} Reisman D., Schultz J., Crawford K., Whittaker M., (2018, April) Algorithmic Impact Assessments: A Practical Framework For Public Agency Accountability. Retrieved from https://ainowinstitute.org/aiareport2018.pdf.

^{^[65]} Sample I., (2017, November 5) Computer says no: why making AIs fair, accountable and transparent is crucial. Retrieved from https://www.theguardian.com/science/2017/nov/05/computer-says-no-why-making-ais-fair-accountable-and-transparent-is-crucial.

^{^[66]} Kroll, J. A., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2016). Accountable algorithms. U. Pa. L. Rev., 165, 633.

^{^[67]} http://www.iso.org/iso/big_data_report-jtc1.pdf

For more details visit https://cis-india.org/internet-governance/blog/ai-in-india-a-policy-agenda

AI in Healthcare

Admin — 2019-09-19T16:15:24Z

The Center for Information Technology and Public Policy (CITAPP) and the International Institute of Information Technology Bangalore (IIITB) invited Radhika Radhakrishnan for a talk at IIIT-Bangalore on September 13, 2019.

In her talk, she critically questioned the dominant narrative of “AI for social good” that has been widely adopted by various stakeholders in India (including the private sector, non-profits, and the Indian State) from a feminist standpoint. Specific to healthcare in India, such a narrative has been employed towards solving development challenges (such as a shortage of medical practitioners in remote regions of the country) through the introduction of AI applications targeted towards the sick-poor. Through her research and fieldwork, she analysed the layers of expropriation and experimentation that come into play when AI technologies become a method of using 'diverse' bodies and medical records of the sick-poor as ‘data’ to train proprietary AI algorithms at a low cost in the absence of effective State regulatory mechanisms. She argued that structural challenges (such as lack of incentives for medical practitioners to join public healthcare) get reframed into opportunities to substitute labour (people) by capital (technology) through innovation of “spectacular technologies” such as AI. Throughout the talk, she also highlighted the methodologies she used to conduct this research.

For more details visit https://cis-india.org/internet-governance/news/ai-in-healthcare

AI for Social Good Summit

Admin — 2018-12-25T01:02:01Z

Arindrajit Basu was a speaker at the event co-organized by Google AI and United Nations ESCAP on December 13, 2018 in Bangkok, Thailand.

Arindrajit spoke at the panel " How can governments use AI in Public Service Delivery" along with Malavika Jayaram, Jake Lucci,Punit Shukla,Simon Schmooly and Gal Oren. He presented CIS research on AI in agriculture in Karnataka-which will be published as part of a compendium documenting case studies worldwide soon.

Click to read more

For more details visit https://cis-india.org/internet-governance/news/unescap-and-google-ai-december-13-bangkok-ai-for-social-good-summit