India’s AI Governance Landscape: Insights from Elite Stakeholder Interviews

Conference paper

Siddharth Mehrotra, Sarah Hladíková
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), ACM, 2026 Apr

DOI: 10.1145/3772363.3798776

Cite

APA Click to copy
Mehrotra, S., & Hladíková, S. (2026). India’s AI Governance Landscape: Insights from Elite Stakeholder Interviews. In Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26). ACM. https://doi.org/10.1145/3772363.3798776

Chicago/Turabian Click to copy
Mehrotra, Siddharth, and Sarah Hladíková. “India’s AI Governance Landscape: Insights from Elite Stakeholder Interviews.” In Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26). ACM, 2026.

MLA Click to copy
Mehrotra, Siddharth, and Sarah Hladíková. “India’s AI Governance Landscape: Insights from Elite Stakeholder Interviews.” Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), ACM, 2026, doi:10.1145/3772363.3798776.

BibTeX Click to copy

@inproceedings{siddharth2026a,
  title = {India’s AI Governance Landscape: Insights from Elite Stakeholder Interviews},
  year = {2026},
  month = apr,
  publisher = {ACM},
  doi = {10.1145/3772363.3798776},
  author = {Mehrotra, Siddharth and Hladíková, Sarah},
  booktitle = {Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26)},
  month_numeric = {4}
}

Abstract:

India’s approach to AI governance differs substantially from West-
ern regulatory frameworks, emphasizing voluntary guidelines and
public-private partnerships over prescriptive legislation. While pol-
icy documents outline this strategy, little empirical research exam-
ines how key stakeholders interpret and implement these frame-
works in practice. We conducted semi-structured interviews with
14 elite stakeholders across government, industry, civil society, and
end-user sectors to understand their perspectives on India’s gover-
nance approach. Our findings reveal significant tensions between
developmental aspirations and ethical safeguards, highlight the
substantial influence of private technology companies in contribut-
ing towards national policy, and expose critical gaps in addressing
algorithmic fairness for India’s diverse social contexts. This work
contributes empirical insights into how India’s distinctive gover-
nance model operates in practice and identifies key challenges for
inclusive AI deployment.