Fairness and Machine Learning¶
Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
Mechanisms¶
- Classification Fairness Review
- The guarding discipline is to pre-commit to metrics and groups before looking, to take input from affected people on which errors hurt, and to treat a result as evidence for revision rather than a certificate — mindful that a formally neutral rule can still produce disparate impact
This sourceExplains that decision practices can have unlawful disparate impact through disproportionate effects even when facially neutral and lacking discriminatory intent.
- The guarding discipline is to pre-commit to metrics and groups before looking, to take input from affected people on which errors hurt, and to treat a result as evidence for revision rather than a certificate — mindful that a formally neutral rule can still produce disparate impact
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Not recorded. Neither this nor any other of the 2 citations of this work carries a recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:f031afb5c6d6 · see in the full table