Fair Decision-making Under Uncertainty
- URL: http://arxiv.org/abs/2301.12364v1
- Date: Sun, 29 Jan 2023 05:42:39 GMT
- Title: Fair Decision-making Under Uncertainty
- Authors: Wenbin Zhang and Jeremy C. Weiss
- Abstract summary: We study a longitudinal censored learning problem subject to fairness constraints.
We show how the newly devised fairness notions involving censored information and the general framework for fair predictions in the presence of censorship allow us to measure and discrimination under uncertainty.
- Score: 1.5688552250473473
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: There has been concern within the artificial intelligence (AI) community and
the broader society regarding the potential lack of fairness of AI-based
decision-making systems. Surprisingly, there is little work quantifying and
guaranteeing fairness in the presence of uncertainty which is prevalent in many
socially sensitive applications, ranging from marketing analytics to actuarial
analysis and recidivism prediction instruments. To this end, we study a
longitudinal censored learning problem subject to fairness constraints, where
we require that algorithmic decisions made do not affect certain individuals or
social groups negatively in the presence of uncertainty on class label due to
censorship. We argue that this formulation has a broader applicability to
practical scenarios concerning fairness. We show how the newly devised fairness
notions involving censored information and the general framework for fair
predictions in the presence of censorship allow us to measure and mitigate
discrimination under uncertainty that bridges the gap with real-world
applications. Empirical evaluations on real-world discriminated datasets with
censorship demonstrate the practicality of our approach.
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