# Walk a Mile in Their Shoes: a New Fairness Criterion for Machine Learning

arXiv: https://arxiv.org/abs/2210.06680 · HTML: https://mlsanity.com/paper/2210.06680/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making](https://mlsanity.com/paper/1806.01059/) (2018)
- [It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks](https://mlsanity.com/paper/2106.05498/) (2021)
- [Discrimination in the Age of Algorithms](https://mlsanity.com/paper/1902.03731/) (2019)
- [Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife](https://mlsanity.com/paper/1311.4555/) (2013)
- [A One-step Approach to Covariate Shift Adaptation](https://mlsanity.com/paper/2007.04043/) (2020)
- [On the Legal Compatibility of Fairness Definitions](https://mlsanity.com/paper/1912.00761/) (2019)
- [The Fairness of Credit Scoring Models](https://mlsanity.com/paper/2205.10200/) (2022)
- [Effective Sample Size, Dimensionality, and Generalization in Covariate Shift Adaptation](https://mlsanity.com/paper/2010.01184/) (2020)
- [Non-portability of Algorithmic Fairness in India](https://mlsanity.com/paper/2012.03659/) (2020)

## Abstract (arXiv)

The old empathetic adage, ``Walk a mile in their shoes,'' asks that one imagine the difficulties others may face. This suggests a new ML counterfactual fairness criterion, based on a textitgroup level: How would members of a nonprotected group fare if their group were subject to conditions in some protected group? Instead of asking what sentence would a particular Caucasian convict receive if he were Black, take that notion to entire groups; e.g. how would the average sentence for all White convicts change if they were Black, but with their same White characteristics, e.g. same number of prior convictions? We frame the problem and study it empirically, for different datasets. Our approach also is a solution to the problem of covariate correlation with sensitive attributes.