Characterizing Fairness Over the Set of Good Models Under Selective
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- URL: http://arxiv.org/abs/2101.00352v2
- Date: Wed, 13 Jan 2021 00:25:36 GMT
- Title: Characterizing Fairness Over the Set of Good Models Under Selective
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- Authors: Amanda Coston and Ashesh Rambachan and Alexandra Chouldechova
- Abstract summary: We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance.
We provide tractable algorithms to compute the range of attainable group-level predictive disparities.
We extend our framework to address the empirically relevant challenge of selectively labelled data.
- Score: 69.64662540443162
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Algorithmic risk assessments are increasingly used to make and inform
decisions in a wide variety of high-stakes settings. In practice, there is
often a multitude of predictive models that deliver similar overall
performance, an empirical phenomenon commonly known as the "Rashomon Effect."
While many competing models may perform similarly overall, they may have
different properties over various subgroups, and therefore have drastically
different predictive fairness properties. In this paper, we develop a framework
for characterizing predictive fairness properties over the set of models that
deliver similar overall performance, or "the set of good models." We provide
tractable algorithms to compute the range of attainable group-level predictive
disparities and the disparity minimizing model over the set of good models. We
extend our framework to address the empirically relevant challenge of
selectively labelled data in the setting where the selection decision and
outcome are unconfounded given the observed data features. We illustrate our
methods in two empirical applications. In a real world credit-scoring task, we
build a model with lower predictive disparities than the benchmark model, and
demonstrate the benefits of properly accounting for the selective labels
problem. In a recidivism risk prediction task, we audit an existing risk score,
and find that it generates larger predictive disparities than any model in the
set of good models.
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