# Non-portability of Algorithmic Fairness in India

arXiv: https://arxiv.org/abs/2012.03659 · HTML: https://mlsanity.com/paper/2012.03659/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://mlsanity.com/paper/2104.02821/) (2021)
- [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023)
- [Socially Aware Bias Measurements for Hindi Language Representations](https://mlsanity.com/paper/2110.07871/) (2021)
- [Towards Responsible Natural Language Annotation for the Varieties of Arabic](https://mlsanity.com/paper/2203.09597/) (2022)
- [Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society](https://mlsanity.com/paper/2403.07904/) (2024)
- [Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditions](https://mlsanity.com/paper/2501.16748/) (2025)
- [Walk a Mile in Their Shoes: a New Fairness Criterion for Machine Learning](https://mlsanity.com/paper/2210.06680/) (2022)

## Abstract (arXiv)

Conventional algorithmic fairness is Western in its sub-groups, values, and optimizations. In this paper, we ask how portable the assumptions of this largely Western take on algorithmic fairness are to a different geo-cultural context such as India. Based on 36 expert interviews with Indian scholars, and an analysis of emerging algorithmic deployments in India, we identify three clusters of challenges that engulf the large distance between machine learning models and oppressed communities in India. We argue that a mere translation of technical fairness work to Indian subgroups may serve only as a window dressing, and instead, call for a collective re-imagining of Fair-ML, by re-contextualising data and models, empowering oppressed communities, and more importantly, enabling ecosystems.