Embedding Principles of Privacy, Transparency and Accountability
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 Aayog Working Document’ or ‘Working Document’). This Working Document was initially prepared for an expert consultation that was held on 21 July 2020. It was later released for comments by stakeholders on the development of a ‘Responsible AI’ policy in India. CCG’s comments and analysis on the Working Document can be accessed here.
In our first post in the series, ‘Building an AI governance framework for India’, we discussed the legal and regulatory implications of the 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 for Responsible AI’. Part II of the series discussed specific Principles for Responsible AI – Safety and Reliability, Equality, and Inclusivity and Non-Discrimination. We explored the constituent elements of these principles and the avenues for incorporating them into the Indian regulatory framework.
In this final post of the series, we will discuss the remaining principles of Privacy, Transparency and Accountability.
Principle of Privacy
Given the diversity of AI systems, the privacy risks which they pose to the individuals, and society as a whole are also varied. These may be be broadly related to :
(i) Data protection and privacy: This relates to privacy implications of the use of data by AI systems and subsequent data protection considerations which arise from this use. There are two broad aspects to think about in terms of the privacy implications from the use of data by AI systems. Firstly, AI systems must be tailored to the legal frameworks for data protection. Secondly, given that AI systems can be used to re-identify anonymised data, the mere anonymisation of data for the training of AI systems may not provide adequate levels of protection for the privacy of an individual.
a) Data protection legal frameworks: Machine learning and AI technologies have existed for decades, however, it was the explosion in the availability of data, which accounts for the advancement of AI technologies in recent years. Machine Learning and AI systems depend upon data for their training. Generally, the more data the system is given, the more it learns and ultimately the more accurate it becomes. The application of existing data protection frameworks to the use of data by AI systems may raise challenges.
In the Indian context, the Personal Data Protection Bill, 2019 (PDP Bill), currently being considered by Parliament, contains some provisions that may apply to some aspects of the use of data by AI systems. One such provision is Clause 22 of the PDP Bill, which requires data fiduciaries to incorporate the seven ‘privacy by design’ principles and embed privacy and security into the design and operation of their product and/or network. However, given that AI systems rely significantly on anonymised personal data, their use of data may not fall squarely within the regulatory domain of the PDP Bill. The PDP Bill does not apply to the regulation of anonymised data at large but the Data Protection Authority has the power to specify a code of practice for methods of de-identification and anonymisation, which will necessarily impact AI technologies’ use of data.
b) Use of AI to re-identify anonymised data: AI applications can be used to re-identify anonymised personal data. To safeguard the privacy of individuals, datasets composed of the personal data of individuals are often anonymised through a de-identification and sampling process, before they are shared for the purposes of training AI systems to address privacy concerns. However, current technology makes it possible for AI systems to reverse this process of anonymisation to re-identify people, having significant privacy implications for an individual’s personal data.
(ii) Impact on society: The impact of the use of AI systems on society essentially relates to broader privacy considerations that arise at a societal level due to the deployment and use of AI, including mass surveillance, psychological profiling, and the use of data to manipulate public opinion. The use of AI in facial recognition surveillance technology is one such AI system that has significant privacy implications for society as a whole. Such AI technology enables individuals to be easily tracked and identified and has the potential to significantly transform expectations of privacy and anonymity in public spaces.
Due to the varying nature of privacy risks and implications caused by AI systems, we will have to design various regulatory mechanisms to address these concerns. It is important to put in place a reporting and investigation mechanism that collects and analyses information on privacy impacts caused by the deployment of AI systems, and privacy incidents that occur in different contexts. The collection of this data would allow actors across the globe to identify common threads of failure and mitigate against potential privacy failures arising from the deployment of AI systems.
To this end, we can draw on a mechanism that is currently in place in the context of reporting and investigating aircraft incidents, as detailed under Annexure 13 of the Convention on International Civil Aviation (Chicago Convention). It lays down the procedure for investigating aviation incidents and a reporting mechanism to share information between countries. The aim of this accident investigation report is not to apportion blame or liability from the investigation, but rather to extensively study the cause of the accident and prevent future incidents.
A similar incident investigation mechanism may be employed for AI incidents involving privacy breaches. With many countries now widely developing and deploying AI systems, such a model of incident investigation would ensure that countries can learn from each other’s experiences and deploy more privacy-secure AI systems.
Principle of Transparency
The concept of transparency is a recognised prerequisite for the realisation of ‘trustworthy AI’. The goal of transparency in ethical AI is to make sure that the functioning of the AI system and resultant outcomes are non-discriminatory, fair, and bias mitigating, and that the AI system inspires public confidence in the delivery of safe and reliable AI innovation and development. Additionally, transparency is also important in ensuring better adoption of AI technology—the more users feel that they understand the overall AI system, the more inclined and better equipped they are to use it.
The level of transparency must be tailored to its intended audience. Information about the working of an AI system should be contextualised to the various stakeholder groups interacting and using the AI system. The Institute of Electrical and Electronics Engineers, a global professional organisation of electronic and electrical engineers, suggested that different stakeholder groups may require varying levels of transparency in accordance with the target group. This means that groups such as users, incident investigators, and the general public would require different standards of transparency depending upon the nature of information relevant for their use of the AI system.
Presently, many AI algorithms are black boxes where automated decisions are taken, based on machine learning over training datasets, and the decision making process is not explainable. When such AI systems produce a decision, human end users don’t know how it arrived at its conclusions. This brings us to two major transparency problems, the public perception and understanding of how AI works, and how much developers actually understand about their own AI system’s decision making process. In many cases, developers may not know, or be able to explain how an AI system makes conclusions or how it has arrived at certain solutions.
This results in a lack of transparency. Some organisations have suggested opening up AI algorithms for scrutiny and ending reliance on opaque algorithms. On the other hand, the NITI Working Document is of the view that disclosing the algorithm is not the solution and instead, the focus should be on explaining how the decisions are taken by AI systems. Given the challenges around explainability discussed above, it will be important for NITI Aayog to discuss how such an approach will be operationalised in practice.
While many countries and organisations are researching different techniques which may be useful in increasing the transparency of an AI system, one of the common suggestions which have gained traction in the last few years is the introduction of labelling mechanisms in AI systems. An example of this is Google’s proposal to use ‘Model Cards’, which are intended to clarify the scope of the AI systems deployment and minimise their usage in contexts for which they may not be well suited.
Model cards are short documents which accompany a trained machine learning model. They enumerate the benchmarked evaluation of the working of an AI system in a variety of conditions, across different cultural, demographic, and intersectional groups which may be relevant to the intended application of the AI system. They also contain clear information on an AI system’s capabilities including the intended purpose for which it is being deployed, conditions under which it has been designed to function, expected accuracy and limitations. Adopting model cards and other similar labelling requirements in the Indian context may be a useful step towards introducing transparency into AI systems.
Principle of Accountability
The Principle of Accountability aims to recognise the responsibility of different organisations and individuals that develop, deploy and use the AI systems. Accountability is about responsibility, answerability and trust. There is no one standard form of accountability, rather this is dependent upon the context of the AI and the circumstances of its deployment.
Holding individuals and entities accountable for harm caused by AI systems has significant challenges as AI systems generally involve multiple parties at various stages of the development process. The regulation of the adverse impacts caused by AI systems often goes beyond the existing regimes of tort law, privacy law or consumer protection law. Some degree of accountability can be achieved by enabling greater human oversight. In order to foster trust in AI and appropriately determine the party who is accountable, it is necessary to build a set of shared principles that clarify responsibilities of each stakeholder involved with the research, development and implementation of an AI system ranging from the developers, service providers and end users.
Accountability has to be ensured at the following stages of an AI system:
(i) Pre-deployment: It would be useful to implement an audit process before the AI system is deployed. A potential mechanism for implementing this could be a multi-stage audit process which is undertaken post design, but before the deployment of the AI system by the developer. This would involve scoping, mapping and testing a potential AI system before it is released to the public. This can include ensuring risk mitigation strategies for changing development environments and ensuring documentation of policies, processes and technologies used in the AI system.
Depending on the nature of the AI system and the potential for risk, regulatory guidelines can be developed prescribing the involvement of various categories of auditors such as internal, expert third party and from the relevant regulatory agency, at various stages of the audit. Such audits which are conducted pre-deployment are aimed at closing the accountability gap which exists currently.
(ii) During deployment: Once the AI system has been deployed, it is important to keep auditing the AI system to note the changes being made/evolution happening in the AI system in the course of its deployment. AI systems constantly learn from the data and evolve to become better and more accurate. It is important that the development team is continuously monitoring the system to capture any errors that may arise, including inconsistencies arising from input data or design features, and address them promptly.
(iii) Post-deployment: Ensuring accountability post-deployment in an AI system can be challenging. The NITI Working Document also recognised that assigning accountability for specific decisions becomes difficult in a scenario with multiple players in the development and deployment of an AI system. In the absence of any consequences for decisions harming others, no one party would feel obligated to take responsibility or take actions to mitigate the effect of the AI systems. Additionally, the lack of accountability also leads to difficulties in grievance redressal mechanisms which can be used to address scenarios where harm has arisen from the use of AI systems.
The Council of Europe, in its guidelines on the human rights impacts of algorithmic systems, highlighted the need for effective remedies to ensure responsibility and accountability for the protection of human rights in the context of the deployment of AI systems. A potential model for grievance redressal is the redressal mechanism suggested in the AI4People’s Ethical Framework for a Good Society report by the Atomium – European Institute for Science, Media and Democracy. The report suggests that any grievance redressal mechanism for AI systems would have to be widely accessible and include redress for harms inflicted, costs incurred, and other grievances caused by the AI system. It must demarcate a clear system of accountability for both organisations and individuals. Of the various redressal mechanisms they have suggested, two significant mechanisms are:
(a) AI ombudsperson: This would ensure the auditing of allegedly unfair or inequitable uses of AI reported by users of the public at large through an accessible judicial process.
(b) Guided process for registering a complaint: This envisions laying down a simple process, similar to filing a Right to Information request, which can be used to bring discrepancies, or faults in an AI system to the notice of the authorities.
Such mechanisms can be evolved to address the human rights concerns and harms arising from the use of AI systems in India.
Conclusion
In early October, the Government of India hosted the Responsible AI for Social Empowerment (RAISE) Summit which has involved discussions around India’s vision and a roadmap for social transformation, inclusion and empowerment through Responsible AI. At the RAISE Summit, speakers underlined the need for adopting AI ethics and a human centred approach to the deployment of AI systems. However, this conversation is still at a nascent stage and several rounds of consultations may be required to build these principles into an Indian AI governance and regulatory framework.
As India enters into the next stage of developing and deploying AI systems, it is important to have multi-stakeholder consultations to discuss mechanisms for the adoption of principles for Responsible AI. This will enable the framing of an effective governance framework for AI in India that is firmly grounded in India’s constitutional framework. While the NITI Aayog Working Document has introduced the concept of ‘Responsible AI’ and the ethics around which AI systems may be designed, it lacks substantive discussion on these principles. Hence, in our analysis, we have explored global views and practices around these principles and suggested mechanisms appropriate for adoption in India’s governance framework for AI. Our detailed analysis of these principles can be accessed in our comments to the NITI Aayog’s Working Document Towards Responsible AI for All.
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