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Artificial Intelligence Apps Risk Entrenching India’s Socio-economic Inequities Expert comment sysadmin 14 March 2018

Artificial intelligence applications will not be a panacea for addressing India’s grand challenges. Data bias and unequal access to technology gains will entrench existing socio-economic fissures.

Artificial intelligence (AI) is high on the Indian government’s agenda. Some days ago, Prime Minister Narendra Modi inaugurated the Wadhwani Institute for Artificial Intelligence, reportedly India’s first research institute focused on AI solutions for social good. In the same week, Niti Aayog CEO Amitabh Kant argued that AI could potentially add $957 billion to the economy and outlined ways in which AI could be a ‘game changer’.

During his budget speech, Finance Minister Arun Jaitley announced that Niti Aayog would spearhead a national programme on AI; with the near doubling of the Digital India budget, the IT ministry also announced the setting up of four committees for AI-related research. An industrial policy for AI is also in the pipeline, expected to provide incentives to businesses for creating a globally competitive Indian AI industry.

Narratives on the emerging digital economy often suffer from technological determinism — assuming that the march of technological transformation has an inner logic, independent of social choice and capable of automatically delivering positive social change. However, technological trajectories can and must be steered by social choice and aligned with societal objectives. Modi’s address hit all the right notes, as he argued that the ‘road ahead for AI depends on and will be driven by human intentions’. Emphasising the need to direct AI technologies towards solutions for the poor, he called upon students and teachers to identify ‘the grand challenges facing India’ – to ‘Make AI in India and for India’.

To do so, will undoubtedly require substantial investments in R&D, digital infrastructure and education and re-skilling. But, two other critical issues must be simultaneously addressed: data bias and access to technology gains.

While computers have been mimicking human intelligence for some decades now, a massive increase in computational power and the quantity of available data are enabling a process of ‘machine learning.’ Instead of coding software with specific instructions to accomplish a set task, machine learning involves training an algorithm on large quantities of data to enable it to self-learn; refining and improving its results through multiple iterations of the same task. The quality of data sets used to train machines is thus a critical concern in building AI applications.

Much recent research shows that applications based on machine learning reflect existing social biases and prejudice. Such bias can occur if the data set the algorithm is trained on is unrepresentative of the reality it seeks to represent. If for example, a system is trained on photos of people that are predominantly white, it will have a harder time recognizing non-white people. This is what led a recent Google application to tag black people as gorillas.

Alternatively, bias can also occur if the data set itself reflects existing discriminatory or exclusionary practices. A recent study by ProPublica found for example that software that was being used to assess the risk of recidivism in criminals in the United States was twice as likely to mistakenly flag black defendants as being at higher risk of committing future crimes.

The impact of such data bias can be seriously damaging in India, particularly at a time of growing social fragmentation. It can contribute to the entrenchment of social bias and discriminatory practices, while rendering both invisible and pervasive the processes through which discrimination occurs. Women are 34 per cent less likely to own a mobile phone than men – manifested in only 14 per cent of women in rural India owning a mobile phone, while only 30 per cent of India’s internet users are women.

Women’s participation in the labour force, currently at around 27 per cent, is also declining, and is one of the lowest in South Asia. Data sets used for machine learning are thus likely to have a marked gender bias. The same observations are likely to hold true for other marginalized groups as well.

Accorded to a 2014 report, Muslims, Dalits and tribals make up 53 per cent of all prisoners in India; National Crime Records Bureau data from 2016 shows in some states, the percentage of Muslims in the incarcerated population was almost three times the percentage of Muslims in the overall population. If AI applications for law and order are built on this data, it is not unlikely that it will be prejudiced against these groups.

(It is worth pointing out that the recently set-up national AI task force is comprised of mostly Hindu men – only two women are on the task force, and no Muslims or Christians. A recent article in the New York Times talked about AI’s ‘white guy problem’; will India suffer from a ‘Hindu male bias’?)

Yet, improving the quality, or diversity, of data sets may not be able to solve the problem. The processes of machine learning and reasoning involve a quagmire of mathematical functions, variables and permutations, the logic of which are not readily traceable or predictable. The dazzle of AI-enabled efficiency gains must not blind us to the fact that while AI systems are being integrated into key socio-economic systems, their accuracy and logic of reasoning have not been fully understood or studied.

The other big challenge stems from the distribution of AI-led technology gains. Even if estimates of AI contribution to GDP are correct, the adoption of these technologies is likely to be in niches within the organized sector. These industries are likely to be capital- rather than labour-intensive, and thus unlikely to contribute to large-scale job creation.

At the same time, AI applications can most readily replace low- to medium-skilled jobs within the organized sector. This is already being witnessed in the outsourcing sector – where basic call and chat tasks are now automated. Re-skilling will be important, but it is unlikely that those who lose their jobs will also be those who are being re-skilled – the long arch of technological change and societal adaptation is longer than that of people’s lives. The contractualization of work, already on the rise, is likely to further increase as large industries prefer to have a flexible workforce to adapt to technological change. A shift from formal employment to contractual work can imply a loss of access to formal social protection mechanisms, increasing the precariousness of work for workers.

The adoption of AI technologies is also unlikely in the short- to medium-term in the unorganized sector, which engages more than 80 per cent of India’s labor force. The cost of developing and deploying AI applications, particularly in relation to the cost of labour, will inhibit adoption. Moreover, most enterprises within the unorganized sector still have limited access to basic, older technologies – two-thirds of the workforce are employed in enterprises without electricity. Eco-system upgrades will be important but incremental. Given the high costs of developing AI-based applications, most start-ups are unlikely to be working towards creating bottom-of-the-pyramid solutions.

Access to AI-led technology gains is thus likely to be heavily differentiated – a few high-growth industries can be expected, but these will not necessarily result in the welfare of labour. Studies show that labour share of national income, especially routine labour, has been declining steadily across developing countries.

We should be clear that new technological applications themselves are not going to transform or disrupt this trend – rather, without adequate policy steering, these trends will be exacerbated.

Policy debates about AI applications in India need to take these two issues seriously. AI applications will not be a panacea for addressing ‘India’s grand challenges’. Data bias and unequal access to technology gains will entrench existing socio-economic fissures, even making them technologically binding.

In addition to developing AI applications and creating a skilled workforce, the government needs to prioritize research that examines the complex social, ethical and governance challenges associated with the spread of AI-driven technologies. Blind technological optimism might entrench rather than alleviate the grand Indian challenge of inequity and growth.

This article was originally published in the Indian Express.

27 August 2020

Visual Abstract

Artificial Intelligence Prediction and Counterterrorism Research paper sysadmin 6 August 2019

The use of AI in counterterrorism is not inherently wrong, and this paper suggests some necessary conditions for legitimate use of AI as part of a predictive approach to counterterrorism on the part of liberal democratic states.

Summary

• The use of predictive artificial intelligence (AI) in countering terrorism is often assumed to have a deleterious effect on human rights, generating spectres of ‘pre-crime’ punishment and surveillance states. However, the well-regulated use of new capabilities may enhance states’ abilities to protect citizens’ right to life, while at the same time improving adherence to principles intended to protect other human rights, such as transparency, proportionality and freedom from unfair discrimination. The same regulatory framework could also contribute to safeguarding against broader misuse of related technologies.
• Most states focus on preventing terrorist attacks, rather than reacting to them. As such, prediction is already central to effective counterterrorism. AI allows higher volumes of data to be analysed, and may perceive patterns in those data that would, for reasons of both volume and dimensionality, otherwise be beyond the capacity of human interpretation. The impact of this is that traditional methods of investigation that work outwards from known suspects may be supplemented by methods that analyse the activity of a broad section of an entire population to identify previously unknown threats.
• Developments in AI have amplified the ability to conduct surveillance without being constrained by resources. Facial recognition technology, for instance, may enable the complete automation of surveillance using CCTV in public places in the near future.
• The current way predictive AI capabilities are used presents a number of interrelated problems from both a human rights and a practical perspective. Where limitations and regulations do exist, they may have the effect of curtailing the utility of approaches that apply AI, while not necessarily safeguarding human rights to an adequate extent.
• The infringement of privacy associated with the automated analysis of certain types of public data is not wrong in principle, but the analysis must be conducted within a robust legal and policy framework that places sensible limitations on interventions based on its results.
• In future, broader access to less intrusive aspects of public data, direct regulation of how those data are used – including oversight of activities by private-sector actors – and the imposition of technical as well as regulatory safeguards may improve both operational performance and compliance with human rights legislation. It is important that any such measures proceed in a manner that is sensitive to the impact on other rights such as freedom of expression, and freedom of association and assembly.

Who gains from artificial intelligence? 27 February 2023 — 5:30PM TO 6:30PM Anonymous (not verified) 6 February 2023 Chatham House and Online

What implications will AI have on fundamental rights and how can societies benefit from this technology revolution?

In recent months, the latest developments in artificial intelligence (AI) have attracted much media attention. These technologies hold a wealth of potential for a wide range of applications, for example, the recent release of OpenAI’s ChatGPT, a text generation model, has shed light on the opportunities these applications hold including to advance scientific research and discovery, enhance search engines and improve key commercial applications.

Yet, instead of generating an evidence-based public debate, this increased interest has also led to discussions on AI technologies which are often alarmist in nature, and in a lot of cases, misleading. They carry the risk of shifting public and policymakers’ attention away from critical societal and legal risks as well as concrete solutions.

This discussion, held in partnership with Microsoft and Sidley Austin LLP, provides an expert-led overview of where the technology stands in 2023. Panellists also reflect on the implications of implementing AI on fundamental rights, the enforcement of current and upcoming legislation and multi-stakeholder pathways to address relevant issues in the AI space.

More specifically, the panel explores:

• What is the current state of the art in the AI field?
• What are the opportunities and challenges presented by generative AI and other innovations?
• What are some of the key, and potentially most disruptive, AI applications to monitor in the near- and mid-term? 
• Which applications would benefit from greater public policy/governance discussions?
• How can current and future policy frameworks ensure the protection of fundamental rights in this new era of AI?
• What is the role of multi-stakeholder collaboration?
• What are the pathways to achieving inclusive and responsible governance of AI?

• How can countries around the world work together to develop frameworks for responsible AI that upholds democratic values and advance AI collaboration across borders?

As with all member events, questions from the audience drive the conversation.

Read the transcript.