Re-imagining Algorithmic Fairness in India and Beyond

• URL: http://arxiv.org/abs/2101.09995v2
• Date: Wed, 27 Jan 2021 02:30:20 GMT
• Title: Re-imagining Algorithmic Fairness in India and Beyond
• Authors: Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, Vinodkumar Prabhakaran
• Abstract summary: We de-center algorithmic fairness and analyse AI power in India. We find that data is not always reliable due to socio-economic factors. We provide a roadmap to re-contextualise data and models, empower oppressed communities, and enable Fair-ML ecosystems.
• Score: 9.667710168953239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional algorithmic fairness is West-centric, as seen in its sub-groups, values, and methods. In this paper, we de-center algorithmic fairness and analyse AI power in India. Based on 36 qualitative interviews and a discourse analysis of algorithmic deployments in India, we find that several assumptions of algorithmic fairness are challenged. We find that in India, data is not always reliable due to socio-economic factors, ML makers appear to follow double standards, and AI evokes unquestioning aspiration. We contend that localising model fairness alone can be window dressing in India, where the distance between models and oppressed communities is large. Instead, we re-imagine algorithmic fairness in India and provide a roadmap to re-contextualise data and models, empower oppressed communities, and enable Fair-ML ecosystems.

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