Data has played a key role in the commercial success of digital economies. At the same time, the fear of its misuse has prompted many countries, including India, to enact data governance laws. Founded on common concerns such as individual privacy and security, the principles and compliances of data protection legislations bear many similarities. This offers India a timely opportunity to guide companies on how to implement data protection and privacy needs in system designs, at a global level. The country can do this by utilszing resources such as the AI handbook on data protection and privacy, a product of Indo-German collaboration, to bridge the gap between engineering and law.
Founded on common concerns such as individual privacy and security, the principles and compliances of data protection legislations bear many similarities.After the European Union passed the General Data Protection Regulation in 2016, a number of countries referred to it to model their own data governance framework. Though the legislations are influenced by GDPR, they have local variations. Amongst others, such laws include India’s proposed Personal Data Protection Bill, California’s Consumer Privacy Act of 2018, Thailand’s Personal Data Protection Act and South Africa's Protection of Personal Information Act. These laws have common data governance principles that emphasise privacy by design, rights of users, basic principles for processing data, civil penalties etc. This makes it possible to envision a fundamental data governance architecture which can fulfil the underlying legal criteria. Startups face challenges in adapting to new data governance regimes across the world, particularly since they may not have the resources to seek constant legal-compliance support. India can make timely capacity building interventions by creating a workforce that can prepare the startup sector to adopt practices that align with new ethical digital norms. Organisations like the OECD and NITI Aayog have already espoused some of the emerging values frameworks, which include bias mitigation, fairness and platform accountability. Additionally, the long-established data processing sector, in India and abroad, will need more support to resolve confusions about the data governance frameworks in various jurisdictions.
India can make timely capacity building interventions by creating a workforce that can prepare the startup sector to adopt practices that align with new ethical digital norms.A good way to do this would be to translate legal requirements into practical implementation. Engineers, who are able to identify sensitive personal data, will be better informed to decide the life cycle of data from the start to the end of the product. Similarly, different levels of workforce in an organisation can be trained to understand their responsibilities vis-a-vis data security. Skilling the digital workforce can set the pace for churning out smart coders, technologists and managers who are able to red flag bad values and innovate new competitive products for data control. Some efforts are already being made to provide technical and managerial guidance to organisations. The AI handbook on data protection and privacy is one of them. The handbook serves as a ready reference guide for developers of AI. In another initiative, experts from different fields came together to create a BIS certified Data Privacy Assurance Standard which proposes a uniform approach to data protection.
Skilling the digital workforce can set the pace for churning out smart coders, technologists and managers who are able to red flag bad values and innovate new competitive products for data control.Data governance laws mark a fundamental shift in the way data-dependent organisations build their products. These bring a renewed focus on building trustworthy products. Those organisations that embed data governance principles into their products right from the start will find ready acceptability in global markets. For example, diversity and qualitative data that is fed into machine-learning algorithms, meant to detect skin cancer, will ensure that they give accurate predictions for all skin types. Similarly, the elimination of historical biases is critical to eradicate gender discrimination in automated hiring softwares. For example, Amazon was forced to redesign its software after the company realised that its algorithms had rejected women candidates.
Diversity and qualitative data that is fed into machine-learning algorithms, meant to detect skin cancer, will ensure that they give accurate predictions for all skin types.A company’s compliance with regulations demonstrates its commitment to security and privacy. As technologies advance towards complex AI, newer ethical values will determine reliability of products. If India is able to take the lead in readying a workforce that is skilled in implementing such values, it can make India a global hub that provides support to value-based digital governance.
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Aditi Chaturvedi is the Head of Legal at Koan Advisory Group New Delhi.
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