This paper extends the work submitted by Gradient to the 2nd Ethics of Data Science Conference on fair regression in a number of ways. Firstly, the methods introduced in the earlier paper for quantifying the fairness of continuous decisions are benchmarked against “gold standard” (but typically intractable) techniques in order to test their efficacy.
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In this paper (to be presented at the second Ethics of Data Science Conference) we study the problem of how to create quantitative, mathematical representations of fairness that can be incorporated into AI systems to promote fair AI-driven decisions. For discrete decisions (such as accepting or rejecting a loan application), there are well established ways to quantify fairness.
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In this post we explain a Bayesian approach to inferring the impact of interventions or actions. We show that representing causality within a standard Bayesian approach softens the boundary between tractable and impossible queries and opens up potential new approaches to causal inference.
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This blog entry contains the executive summary of Gradient Institute's new White Paper. The full paper can be found here. This White Paper examines four key challenges that must be addressed to make progress towards developing ethical artificial intelligence (AI) systems.
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Gradient Institute, along with Verge Labs and Women in Machine Learning and Data Science, will be hosting two free meetup events to coincide with the NeurIPS conference. Curated content from NeurIPS will be streamed and discussed by local researchers and practitioners.
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