Building an AI Governance Framework for India, Part II

Embedding Principles of Safety, Equality and Non-Discrimination

This post has been authored by Jhalak M. Kakkar and Nidhi Singh

In July 2020, the NITI Aayog released a draft Working Document entitled “Towards Responsible AI for All” (hereafter ‘NITI Working Document’ or ‘Working Document’). This Working Document was initially prepared for an expert consultation held on 21 July 2020. It was later released for comments by stakeholders on the development of a ‘Responsible AI’ policy in India. CCG responded with comments to the Working Document, and our analysis can be accessed here.

In our previous post on building an AI governance framework for India, we discussed the legal and regulatory implications of the proposed Working Document and argued that India’s approach to regulating AI should be (1) firmly grounded in its Constitutional framework and (2) based on clearly articulated overarching principles. While the NITI Working Document introduces certain principles, it does not go into any substantive details on what the adoption of these principles into India’s regulatory framework would entail.

We will now examine these ‘Principles for Responsible AI’, their constituent elements and avenues for incorporating them into the Indian regulatory framework. The NITI Working Document proposed the following seven ‘Principles for Responsible AI’ to guide India’s regulatory framework for AI systems: 

1. Safety and reliability
2. Equality
3. Inclusivity and Non-Discrimination
4. Privacy and Security 
5. Transparency
6. Accountability
7. Protection and Reinforcement of Positive Human Values. 

This post explores the principles of Safety and Reliability, Equality, and Inclusivity and Non-Discrimination. A subsequent post will discuss the principles of Privacy and Security, Transparency, Accountability and the Protection and Reinforcement of Positive Human Values.

Principle of Safety and Reliability

The Principle of Reliability and Safety aims to ensure that AI systems operate reliably in accordance with their intended purpose throughout their lifecycle and ensures the security, safety and robustness of an AI system. It requires that AI systems should not pose unreasonable safety risks, should adopt safety measures which are proportionate to the potential risks, should be continuously monitored and tested to ensure compliance with their intended purpose, and should have a continuous risk management system to address any identified problems. 

Here, it is important to note the distinction between safety and reliability. The reliability of a system relates to the ability of an AI system to behave exactly as its designers have intended and anticipated. A reliable system would adhere to the specifications it was programmed to carry out. Reliability is therefore, a measure of consistency and establishes confidence in the safety of a system. Whereas, safety refers to an AI system’s ability to do what it is supposed to do without harming users (human physical integrity), resources or the environment.

Human oversight: An important aspect of ensuring the safety and reliability of AI systems is the presence of human oversight over the system. Any regulatory framework that is developed in India to govern AI systems must incorporate norms that specify the circumstances and degree to which human oversight is required over various AI systems. 

The level of involvement of human oversight would depend upon the sensitivity of the function and potential for significant impact on an individual’s life which the AI system may have. For example, AI systems deployed in the context of the provision of government benefits should have a high level of human oversight. Decisions made by the AI system in this context should be reviewed by a human before being implemented. Other AI systems may be deployed in contexts that do not need constant human involvement. However, these systems should have a mechanism in place for human review if a question is subsequently raised for review by, say a user. An example of this may be vending machines which have simple algorithms. Hence, the purpose for which the system is deployed and the impact it could have on individuals would be relevant factors in determining if ‘human in the loop’, ‘human on the loop’, or any other oversight mechanism is appropriate. 

Principle of Equality

The principle of equality holds that everyone, irrespective of their status in the society, should get the same opportunities and protections with the development of AI systems. 

Implementing equality in the context of AI systems essentially requires three components: 

(i) Protection of human rights: AI instruments developed across the globe have highlighted that the implementation of AI would pose risks to the right to equality, and countries would have to take steps to mitigate such risks proactively. 

(ii) Access to technology: The AI systems should be designed in a way to ensure widespread access to technology, so that people may derive benefits from AI technology.

(iii) Guarantees of equal opportunities through technology: The guarantee of equal opportunity relies upon the transformative power of AI systems to “help eliminate relationships of domination between groups and people based on differences of power, wealth, or knowledge” and “produce social and economic benefits for all by reducing social inequalities and vulnerabilities.” AI systems will have to be designed and deployed such that they further the guarantees of equal opportunity and do not exacerbate and further entrench existing inequality.

The development, use and deployment of AI systems in society would pose the above-mentioned risks to the right to equality, and India’s regulatory framework for AI must take steps to mitigate such risks proactively.

Principle of Inclusivity and Non-Discrimination

The idea of non-discrimination mostly arises out of technical considerations in the context of AI. It holds that non-discrimination and the prevention of bias in AI should be mitigated in the training data, technical design choices, or the technology’s deployment to prevent discriminatory impacts. 

Examples of this can be seen in data collection in policing, where the disproportionate attention paid to neighbourhoods with minorities, would show higher incidences of crime in minority neighbourhoods, thereby skewing AI results. Use of AI systems becomes safer when they are trained on datasets that are sufficiently broad, and the datasets encompass the various scenarios in which the system is envisaged to be deployed. Additionally, datasets should be developed to be representative and hence avoid discriminatory outcomes from the use of the AI system. 

Another example of this can be semi-autonomous vehicles which experience higher accident rates among dark-skinned pedestrians due to the software’s poorer performance in recognising darker-skinned individuals. This can be traced back to training datasets, which contained mostly light-skinned people. The lack of diversity in the data set can lead to discrimination against specific groups in society. To ensure effective non-discrimination, AI policies must be truly representative of the society in its training data and ensure that no section of the populace is either over-represented or under-represented, which may skew the data sets. While designing the AI systems for deployment in India, the constitutional rights of individuals should be used as central values around which the AI systems are designed. 

In order to implement inclusivity in AI, the diversity of the team involved in design as well as the diversity of the training data set would have to be assessed. This would involve the creation of guidelines under India’s regulatory framework for AI to help researchers and programmers in designing inclusive data sets, measuring product performance on the parameter of inclusivity, selecting features to avoid exclusion and testing new systems through the lens of inclusivity.

Checklist Model: To address the challenges of non-discrimination and inclusivity a potential model which can be adopted in India’s regulatory framework for AI would be the ‘Checklist’. The European Network of Equality Bodies (EQUINET), in its recent report on ‘Meeting the new challenges to equality and non-discrimination from increased digitisation and the use of Artificial Intelligence’ provides a checklist to assess whether an AI system is complying with the principles of equality and non-discrimination. The checklist consists of several broad categories, with a focus on the deployment of AI technology in Europe. This includes heads such as direct discrimination, indirect discrimination, transparency, other types of equity claims, data protection, liability issues, and identification of the liable party. 

The list contains a series of questions which judges whether an AI system meets standards of equality, and identifies any potential biases it may have. For example, the question “Does the artificial intelligence system treat people differently because of a protected characteristic?” includes the parameters of both direct data and proxies. If the answer to the question is yes, the system would be identified as indulging in indirect bias. A similar checklist system, which has been contextualised for India, can be developed and employed in India’s regulatory framework for AI. 

Way forward

This post highlights some of the key aspects of the principles of Safety and Reliability, Equality, and Inclusivity and Non-Discrimination. Integration of these principles which have been identified in the NITI Working Document into India’s regulatory framework requires that we first clearly define their content, scope and ambit to identify the right mechanisms to operationalise them. Given the absence of any exploration of the content of these AI principles or the mechanism for their implementation in India in the NITI Working Document, we have examined the relevant international literature surrounding the adoption of AI ethics and suggested mechanisms for their adoption. The NITI Working Document has spurred discussion around designing an effective regulatory framework for AI. However, these discussions are at a preliminary stage and there is a need to develop a far more nuanced proposal for a regulatory framework for AI.

Over the last week, India has hosted the Responsible AI for Social Empowerment (RAISE) Summit which has involved discussions around India’s vision and roadmap for social transformation, inclusion and empowerment through Responsible AI. As we discuss mechanisms for India to effectively harness the economic potential of AI, we also need to design an effective framework to address the massive regulatory challenges emerging from the deployment of AI—simultaneously, and not as an afterthought post-deployment. While a few of the RAISE sessions engaged with certain aspects of regulating AI, there still remains a need for extensive, continued public consultations with a cross section of stakeholders to embed principles for Responsible AI in the design of an effective AI regulatory framework for India. 

For a more detailed discussion on these principles and their integration into the Indian context, refer to our comments to the NITI Aayog here. 

Building an AI governance framework for India

This post has been authored by Jhalak M. Kakkar and Nidhi Singh

In July 2020, the NITI Aayog released a “Working Document: Towards Responsible AI for All” (“NITI Working Document/Working Document”). The Working Document was initially prepared for an expert consultation held on 21 July 2020. It was later released for comments by stakeholders on the development of a ‘Responsible AI’ policy in India. CCG responded with comments to the Working Document, and our analysis can be accessed here.

The Working Document highlights the potential of Artificial Intelligence (“AI”) in the Indian context. It attempts to identify the challenges that will be faced in the adoption of AI and makes some recommendations on how to address these challenges. The Working Document emphasises the economic potential of the adoption of AI in boosting India’s annual growth rate, its potential for use in the social sector (‘AI for All’) and the potential for India to export relevant social sector products to other emerging economies (‘AI Garage’). 

However, this is not the first time that the NITI Aayog has discussed the large-scale adoption of AI in India. In 2018, the NITI Aayog released a discussion paper on the “National Strategy for Artificial Intelligence” (“National Strategy”). Building upon the National Strategy, the Working Document attempts to delineate ‘Principles for Responsible AI’ and identify relevant policy and governance recommendations. 

Any framework for the regulation of AI systems needs to be based on clear principles. The ‘Principles for Responsible AI’ identified by the Working Document include the principles of safety and reliability, equality, inclusivity and non-discrimination, privacy and security, transparency, accountability, and the protection and reinforcement of positive human values. While the NITI Working Document introduces these principles, it does not go into any substantive details on the regulatory approach that India should adopt and what the adoption of these principles into India’s regulatory framework would entail. 

In a series of posts, we will discuss the legal and regulatory implications of the proposed Working Document and more broadly discuss the regulatory approach India should adopt to AI and the principles India should embed in it. In this first post, we map out key considerations that should be kept in mind in order to develop a comprehensive regulatory regime to govern the adoption and deployment of AI systems in India. Subsequent posts will discuss the various ‘Principles for Responsible AI’, their constituent elements and how we should think of incorporating them into the Indian regulatory framework.

Approach to building an AI regulatory framework 

While the adoption of AI has several benefits, there are several potential harms and unintended risks if the technology is not assessed adequately for its alignment with India’s constitutional principles and its impact on the safety of individuals. Depending upon the nature and scope of the deployment of an AI system, its potential risks can include the discriminatory impact on vulnerable and marginalised communities, and material harms such as the negative impact on the health and safety of individuals. In the case of deployments by the State, risks include violation of the fundamental rights to equality, privacy, freedom of assembly and association, and freedom of speech and expression. 

We highlight some of the regulatory considerations that should be considered below:

Anchoring AI regulatory principles within the constitutional framework of India

The use of AI systems has raised concerns about their potential to violate multiple rights protected under the Indian Constitution such as the right against discrimination, the right to privacy, the right to freedom of speech and expression, the right to assemble peaceably and the right to freedom of association. Any regulatory framework put in place to govern the adoption and deployment of AI technology in India will have to be in consonance with its constitutional framework. While the NITI Working Document does refer to the idea of the prevailing morality of India and its relation to constitutional morality, it does not comprehensively address the idea of framing AI principles in compliance with India’s constitutional principles.

For instance, the government is seeking to acquire facial surveillance technology, and the National Strategy discusses the use of AI-powered surveillance applications by the government to predict crowd behaviour and for crowd management. The use of AI powered surveillance systems such as these needs to be balanced with their impact on an individual’s right to freedom of speech and expression, privacy and equality. Operational challenges surrounding accuracy and fairness in these systems raise further concerns. Considering the risks posed to the privacy of individuals, the deployment of these systems by the government, if at all, should only be done in specific contexts for a particular purpose and in compliance with the principles laid down by the Supreme Court in the Puttaswamy case.

In the context of AI’s potential to exacerbate discrimination, it would be relevant to discuss the State’s use of AI systems for the sentencing of criminals and assessing recidivism. AI systems are trained on existing datasets. These datasets tend to contain historically biased, unequal and discriminatory data. We have to be cognizant of the propensity for historical bias’ and discrimination getting imported into AI systems and their decision making. This could further reinforce and exacerbate the existing discrimination in the criminal justice system towards marginalised and vulnerable communities, and result in a potential violation of their fundamental rights.

The National Strategy acknowledges the presence of such biases and proposes a technical approach to reduce bias. While such attempts are appreciable in their efforts to rectify the situation and yield fairer outcomes, such an approach disregards the fact that these datasets are biased because they arise from a biased, unequal and discriminatory world. As we seek to build effective regulation to govern the use and deployment of AI systems, we have to remember that these are socio-technical systems that reflect the world around us and embed the biases, inequality and discrimination inherent in the Indian society. We have to keep this broader Indian social context in mind as we design AI systems and create regulatory frameworks to govern their deployment. 

While, the Working Document introduces the principles for responsible AI such as equality, inclusivity and non-discrimination, and privacy and security, there needs to be substantive discussion around incorporating these principles into India’s regulatory framework in consonance with constitutional guaranteed rights.

Regulatory Challenges in the adoption of AI in India

As India designs a regulatory framework to govern the adoption and deployment of AI systems, it is important that we keep the following in focus: 

• Heightened threshold of responsibility for government or public sector deployment of AI systems

The EU is considering adopting a risk-based approach for regulation of AI, with heavier regulation for high-risk AI systems. The extent of risk factors such as safety, consumer rights and fundamental rights are assessed by looking at the sector of deployment and the intended use of the AI system. Similarly, India must consider the adoption of a higher regulatory threshold for the use of AI by at least government institutions, given their potential for impacting citizen’s rights. Government use of AI systems that have the potential of severely impacting citizens’ fundamental rights include the use of AI in the disbursal of government benefits, surveillance, law enforcement and judicial sentencing. 

• Need for overarching principles based AI regulatory framework

Different sectoral regulators are currently evolving regulations to address the specific challenges posed by AI in their sector. While it is vital to harness the domain expertise of a sectoral regulator and encourage the development of sector-specific AI regulations, such piecemeal development of AI principles can lead to fragmentation in the overall approach to regulating AI in India. Therefore, to ensure uniformity in the approach to regulating AI systems across sectors, it is crucial to put in place a horizontal overarching principles-based framework. 

• Adaptation of sectoral regulation to effectively regulate AI

In addition to an overarching regulatory framework which forms the basis for the regulation of AI, it is equally important to envisage how this framework would work with horizontal or sector-specific laws such as consumer protection law and the applicability of product liability to various AI systems. Traditionally consumer protection and product liability regulatory frameworks have been structured around fault-based claims. However, given the challenges concerning explainability and transparency of decision making by AI systems, it may be difficult to establish the presence of defects in products and, for an individual who has suffered harm, to provide the necessary evidence in court. Hence, consumer protection laws may have to be adapted to stay relevant in the context of AI systems. Even sectoral legislation regulating the use of motor vehicles, such as the Motor Vehicles Act, 1988 would have to be modified to enable and regulate the use of autonomous vehicles and other AI transport systems. 

• Contextualising AI systems for both their safe development and use

To ensure the effective and safe use of AI systems, they have to be designed, adapted and trained on relevant datasets depending on the context in which they will be deployed. The Working Document envisages India being the AI Garage for 40% of the world – developing AI solutions in India which can then be deployed in other emerging economies. Additionally, India will likely import AI systems developed in countries such as the US, EU and China to be deployed within the Indian context. Both scenarios involve the use of AI systems in a context distinct from the one in which they have been developed. Without effectively contextualising socio-technical systems like AI systems to the environment they are to be deployed in, there are enhanced safety, accuracy and reliability concerns. Regulatory standards and processes need to be developed in India to ascertain the safe use and deployment of AI systems that have been developed in contexts that are distinct from the ones in which they will be deployed. 

The NITI Working Document is the first step towards an informed discussion on the adoption of a regulatory framework to govern AI technology in India. However, there is a great deal of work to be done. Any regulatory framework developed by India to govern AI must balance the benefits and risks of deploying AI, diminish the risk of any harm and have a consumer protection framework in place to adequately address any harm that may arise. Besides this, the regulatory framework must ensure that the deployment and use of AI systems are in consonance with India’s constitutional scheme.