Statistical inference for individual fairness



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ICLR Machine Learning

As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e.g., gender and racial biases) has come to the fore of the public’s attention. In this paper, we focus on the problem of detecting violations of individual fairness in ML models. We formalize the problem as measuring the susceptibility of ML models against a form of adversarial attack and develop a suite of inference tools for the adversarial cost function. The tools allow auditors to assess the individual fairness of ML models in a statistically-principled way: form confidence intervals for the worst-case performance differential between similar individuals and test hypotheses of model fairness with (asymptotic) non-coverage/Type I error rate control. We demonstrate the utility of our tools in a real-world case study.

This paper has been published at ICLR 2021

Please cite our work using the BibTeX below.

      title={Statistical inference for individual fairness}, 
      author={Subha Maity and Songkai Xue and Mikhail Yurochkin and Yuekai Sun},
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