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The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations. (arXiv:2205.03295v1 [cs.LG])
May 9, 2022, 1:11 a.m. | Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi
cs.LG updates on arXiv.org arxiv.org
Machine learning models in safety-critical settings like healthcare are often
blackboxes: they contain a large number of parameters which are not transparent
to users. Post-hoc explainability methods where a simple, human-interpretable
model imitates the behavior of these blackbox models are often proposed to help
users trust model predictions. In this work, we audit the quality of such
explanations for different protected subgroups using real data from four
settings in finance, healthcare, college admissions, and the US justice system.
Across two …
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