Confidence Intervals for Testing Disparate Impact in Fair Learning

07/17/2018

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We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning. We aim at promoting the use of confidence intervals when testing the so-called group disparate impact. We illustrate on some examples the importance of using confidence intervals and not a single value.

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Confidence Intervals for Testing Disparate Impact in Fair Learning

Extreme value theory based confidence intervals for the parameters of a symmetric Lévy-stable distribution

Simple Buehler-optimal confidence intervals on the average success probability of independent Bernoulli trials

Statistical inference for piecewise normal distributions and stochastic variational inequalities

Confidence regions for univariate and multivariate data using permutation tests

Confidence Intervals for Recursive Journal Impact Factors

Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance

Confidence intervals for AB-test

Confidence Intervals for Testing Disparate Impact in Fair Learning

Related Research

Extreme value theory based confidence intervals for the parameters of a symmetric Lévy-stable distribution

Simple Buehler-optimal confidence intervals on the average success probability of independent Bernoulli trials

Statistical inference for piecewise normal distributions and stochastic variational inequalities

Confidence regions for univariate and multivariate data using permutation tests

Confidence Intervals for Recursive Journal Impact Factors

Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance

Confidence intervals for AB-test