Affirmative Algorithms: The Legal Grounds for Fairness as Awareness

• URL: http://arxiv.org/abs/2012.14285v1
• Date: Fri, 18 Dec 2020 22:53:20 GMT
• Title: Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
• Authors: Daniel E. Ho and Alice Xiang
• Abstract summary: We discuss how such approaches will likely be deemed "algorithmic affirmative action" We argue that the government-contracting cases offer an alternative grounding for algorithmic fairness. We call for more research at the intersection of algorithmic fairness and causal inference to ensure that bias mitigation is tailored to specific causes and mechanisms of bias.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques. We make three contributions. First, we discuss how such approaches will likely be deemed "algorithmic affirmative action," posing serious legal risks of violating equal protection, particularly under the higher education jurisprudence. Such cases have increasingly turned toward anticlassification, demanding "individualized consideration" and barring formal, quantitative weights for race regardless of purpose. This case law is hence fundamentally incompatible with fairness in machine learning. Second, we argue that the government-contracting cases offer an alternative grounding for algorithmic fairness, as these cases permit explicit and quantitative race-based remedies based on historical discrimination by the actor. Third, while limited, this doctrinal approach also guides the future of algorithmic fairness, mandating that adjustments be calibrated to the entity's responsibility for historical discrimination causing present-day disparities. The contractor cases provide a legally viable path for algorithmic fairness under current constitutional doctrine but call for more research at the intersection of algorithmic fairness and causal inference to ensure that bias mitigation is tailored to specific causes and mechanisms of bias.

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