Big Data's Disparate Impact¶
Barocas, S. (2016). Big Data's Disparate Impact. California Law Review, 104(3), 671-732.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Equity
- The abstraction generalizes beyond formal law to any institutional context where case-by-case judgment and remedial discretion must operate alongside rule systems: algorithmic exception-handling, software engineering's case-specific patches, organizational review of policy-rule enforcement, ethical committees adjudicating borderline cases, and dispute-resolution processes where rigid rules would fail the underlying purpose of fairness, a substrate-extension that Barocas and Selbst (2016) develop in their analysis of disparate impact in algorithmic systems.
This sourceShows how data-driven decision systems produce discriminatory outcomes through training data, feature selection, and proxies, and argues solving this requires reexamining 'discrimination' and 'fairness'.
- The abstraction generalizes beyond formal law to any institutional context where case-by-case judgment and remedial discretion must operate alongside rule systems: algorithmic exception-handling, software engineering's case-specific patches, organizational review of policy-rule enforcement, ethical committees adjudicating borderline cases, and dispute-resolution processes where rigid rules would fail the underlying purpose of fairness, a substrate-extension that Barocas and Selbst (2016) develop in their analysis of disparate impact in algorithmic systems.
- Fairness
- This reframing allows organizations to audit systems (hiring algorithms, resource allocation, performance evaluation) against explicit fairness metrics, identify misalignment, and adjust, as Barocas and Selbst (2016) document for "big data's disparate impact" and the auditing of algorithmic decision systems.
This sourceShows how data-mining/algorithmic systems produce discriminatory outcomes through training data, feature selection, and proxies, and argues for auditing such systems and rethinking 'discrimination' and 'fairness.'
- This reframing allows organizations to audit systems (hiring algorithms, resource allocation, performance evaluation) against explicit fairness metrics, identify misalignment, and adjust, as Barocas and Selbst (2016) document for "big data's disparate impact" and the auditing of algorithmic decision systems.
Mechanisms¶
- Risk Score Proxy Metric
- Its failure mode is uniquely ethical: a proxy can encode and launder the very attributes it must not use
This sourceShows how neutral-seeming variables can proxy protected-class membership and yield systematically less favorable outcomes behind an apparently objective model.
- Its failure mode is uniquely ethical: a proxy can encode and launder the very attributes it must not use
Verification¶
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Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
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