Fairness and Machine Learning: Limitations and Opportunities¶
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning: Limitations and Opportunities: Limitations and Opportunities.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
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.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:a566d7ce68d0 · see in the full table