Related papers: Fairness Evaluation in Presence of Biased Noisy Labels

Fairness Evaluation in Presence of Biased Noisy Labels

URL: http://arxiv.org/abs/2003.13808v1
Date: Mon, 30 Mar 2020 20:47:00 GMT
Title: Fairness Evaluation in Presence of Biased Noisy Labels
Authors: Riccardo Fogliato, Max G'Sell, Alexandra Chouldechova
Abstract summary: We propose a sensitivity analysis framework for assessing how assumptions on the noise across groups affect the predictive bias properties of the risk assessment model. Our experimental results on two real world criminal justice data sets demonstrate how even small biases in the observed labels may call into question the conclusions of an analysis based on the noisy outcome.
Score: 84.12514975093826
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Risk assessment tools are widely used around the country to inform decision making within the criminal justice system. Recently, considerable attention has been devoted to the question of whether such tools may suffer from racial bias. In this type of assessment, a fundamental issue is that the training and evaluation of the model is based on a variable (arrest) that may represent a noisy version of an unobserved outcome of more central interest (offense). We propose a sensitivity analysis framework for assessing how assumptions on the noise across groups affect the predictive bias properties of the risk assessment model as a predictor of reoffense. Our experimental results on two real world criminal justice data sets demonstrate how even small biases in the observed labels may call into question the conclusions of an analysis based on the noisy outcome.

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