Fairness and Machine Learning: Limitations and Opportunities¶
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning: Limitations and Opportunities: Limitations and Opportunities.
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Primes¶
- Distributional Effects
- The welfare-economics move of decomposing aggregate effects by subgroup transfers directly to ML fairness, where group-conditional accuracy is a distributional-effects analysis: the income decile becomes the demographic group, the per-unit effect becomes per-group model performance, and the same disaggregation that exposes a regressive tax exposes a discriminatory classifier.
This sourceDevelops group-conditional performance metrics and the disaggregation of average accuracy into per-group rates.
- The welfare-economics move of decomposing aggregate effects by subgroup transfers directly to ML fairness, where group-conditional accuracy is a distributional-effects analysis: the income decile becomes the demographic group, the per-unit effect becomes per-group model performance, and the same disaggregation that exposes a regressive tax exposes a discriminatory classifier.
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