Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making
By Vidushi Marda. 2018
114 citations recorded by the source corpus.
Artificial intelligence (AI) is an emerging focus area of policy development in India. The country's regional influence, burgeoning AI industry and ambitious governmental initiatives around AI make it an important jurisdiction to consider, regardless of where the reader of this article lives. Even as existing policy processes intend to encourage the rapid development of AI for economic growth and social good, an overarching trend persists in India, and several other jurisdictions: the limitations and risks of data-driven decisions still feature as retrospective considerations for development and deployment of AI applications. This article argues that the technical limitations of AI systems should be reckoned with at the time of developing policy, and the societal and ethical concerns that arise due to such limitations should be used to inform what policy processes aspire to achieve. It proposes a framework for such deliberation to occur, by analysing the three main stages of bringing machine learning (the most popular subset of AI techniques) to deployment—the data, model and application stage. It is written against the backdrop of India's current AI policy landscape, and applies the proposed framework to ongoing sectoral challenges in India. With a view to influence existing policy deliberation in the country, it focuses on potential risks that arise from data-driven decisions in general, and in the Indian context in particular. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges'.
Subfields: Artificial Intelligence, Information Systems, Safety Research, AI Ethics, Fairness & Society, Social Sciences & Humanities
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ben Hutchinson
- Vinodkumar Prabhakaran
- Maranke Wieringa
- Thilo Hagendorff
- Vidushi Marda
- Luke Munn
- Emily L. Denton
In Information Systems
- Ben Hutchinson
- Maranke Wieringa
- Nithya Sambasivan
- Vinodkumar Prabhakaran
- Erin Arnesen
- Tulsee Doshi
- Thilo Hagendorff
- Vidushi Marda
In Safety Research
- Vinodkumar Prabhakaran
- Maranke Wieringa
- Nithya Sambasivan
- Ben Hutchinson
- Thilo Hagendorff
- Vidushi Marda
- Luke Munn
In AI Ethics, Fairness & Society
- Maranke Wieringa
- Nithya Sambasivan
- Ben Hutchinson
- Thilo Hagendorff
- Vinodkumar Prabhakaran
- Vidushi Marda
In Social Sciences & Humanities
- Maranke Wieringa
- Nithya Sambasivan
- Erin Arnesen
- Ben Hutchinson
- Thilo Hagendorff
- Tulsee Doshi
- Vinodkumar Prabhakaran
- Vidushi Marda
Citations in the corpus
- Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “opportunities for AI in India [104], proposed policy deliberation frameworks that take into account the unique policy landscape of India [126], and questioned the intrusive data collection practices through Aadhaar (biometric-based unique identity for Indians) in India [158, 159].”
- Artificial Intelligence Governance and Ethics: Global Perspectives (2019, background): “The second is Make in India, under which the Government of India is prioritising AI technology designed and developed in India, and the third is the Smart Cities Mission (Marda 2018).”
- What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, background): “While, we do find several papers with Global South perspectives in the corpus (e.g. [100, 104], this is something that we take in account when evaluating the methodology elsewhere.”