India and Singapore are among the founding members of the Global Partnership on Artificial Intelligence (GPAI)–a multi-stakeholder initiative that aims to bridge the gap between theory and practice on AI by supporting innovative research and applied activities on AI-related priorities. Data governance is one such priority area. Several factors influence developing a data governance framework – adequacy of existing data infrastructures, availability of standardised data sets and the regulatory balance between data availability and privacy are especially important. India, for example, is data-rich but not ‘data intelligent’. Rich data available through publicly funded initiatives are not publicly available. India should, therefore, focus on creating open data platforms where data generated through government initiatives, for example, can be accessed and leveraged easily by other stakeholders. Data privacy is a concern, especially while accessing data generated by the private sector. While the government is looking to bring a new Personal Data Protection Bill, it should also look at sector-specific regulatory mechanisms used in countries such as Norway. On a broader level, India should look at an AI market place especially where data sets come for a price. This is necessary for Indian start-ups to raise venture capital (VC) funds. The inability to quantify the costs of procuring a specific data set to deploy AI solutions can be a hindrance to raising VC funding. Finally, India must move beyond developing AI applications and introduce policies to help cultivate technical skills and foster innovation.
