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Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse

机译：公平失败的地方：数据，算法和反歧视话语的局限性

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摘要

Problems of bias and fairness are central to data justice, as they speak directly to the threat that big data' and algorithmic decision-making may worsen already existing injustices. In the United States, grappling with these problems has found clearest expression through liberal discourses of rights, due process, and antidiscrimination. Work in this area, however, has tended to overlook certain established limits of antidiscrimination discourses for bringing about the change demanded by social justice. In this paper, I engage three of these limits: 1) an overemphasis on discrete bad actors', 2) single-axis thinking that centers disadvantage, and 3) an inordinate focus on a limited set of goods. I show that, in mirroring some of antidiscrimination discourse's most problematic tendencies, efforts to achieve fairness and combat algorithmic discrimination fail to address the very hierarchical logic that produces advantaged and disadvantaged subjects in the first place. Finally, I conclude by sketching three paths for future work to better account for the structural conditions against which we come to understand problems of data and unjust discrimination in the first place.
机译：偏见和公平问题对于数据公正至关重要，因为它们直接说明了大数据和算法决策可能加剧已经存在的不公正现象的威胁。在美国，通过权利的自由论述，正当程序和反歧视，找到了解决这些问题的最清晰的表述。但是，这一领域的工作往往忽视了反歧视话语在实现社会正义所要求的变革方面的某些既定局限性。在本文中，我限制了其中三个限制：1）过分强调离散的不良行为者； 2）以单轴思维为中心的劣势； 3）过分关注有限的商品。我表明，在反映一些反歧视话语最成问题的趋势时，为实现公平和打击算法歧视所做的努力未能解决首先产生优势和劣势主体的非常层级的逻辑。最后，最后我概述了未来工作的三种途径，以便更好地说明我们首先要了解数据问题和不公正歧视的结构条件。

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