Fairness Considered Harmful: On the Non-portability of Fair-ML in India
Fairness Considered Harmful: On the Non-portability of Fair-ML in India
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公平被认为是有害的：论公平机器学习在印度的不可移植性
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Vinodkumar Prabhakaran
中科院分区：
文献类型：
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作者：
Nithya Sambasivan;Erin Arnesen;B. Hutchinson;Vinodkumar Prabhakaran
传统的算法公平性在子组、价值观和优化方面都是西方的。在本文中，我们询问这种主要西方对算法公平性的假设如何移植到印度等不同的地缘文化背景。基于对印度学者的 36 名专家访谈以及对印度新兴算法部署的分析，我们确定了三组挑战，这些挑战跨越了机器学习模型与印度受压迫社区之间的巨大差距。我们认为，仅仅将技术公平工作转移到印度的子群体可能只能起到门面的作用，相反，我们呼吁通过重新关联数据和模型来集体重新构想 Fair-ML，赋予受压迫社区权力，更重要的是，支持生态系统。
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.