Libra: A Prototype for Provenance-Aware Fairness Audit Parameters — Implementation Review and Descriptive Sensitivity Analysis

Fairness audits require choices about groups, reference populations, outcome definitions, and decision thresholds. This unreviewed technical preprint presents Libra, a prototype that stores some of those choices with process metadata and applies a selected threshold and reference group in a group-outcome audit. It reviews implementation boundaries and reports descriptive sensitivity analyses using one 2024 Michigan HMDA lender extract and a historical ProPublica COMPAS extract. The HMDA Black-to-White origination ratio is 0.8174 across retained action codes and 0.7162 for originated-versus-denied records under the stated filters; these are different administrative-outcome estimands, not AI-model performance measures. The paper uses no community participant data, validates no community-defined standard, and establishes no government endorsement or adoption. The public companion package documents source acquisition, scripts, results, and their limitations.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-13
DOI
https://doi.org/10.5281/zenodo.23328062
Primary Topic
Ethics and Social Impacts of AI
Type
preprint
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preprint

Libra: A Prototype for Provenance-Aware Fairness Audit Parameters — Implementation Review and Descriptive Sensitivity Analysis

Matthew Jackson
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
preprint

Libra: A Prototype for Provenance-Aware Fairness Audit Parameters — Implementation Review and Descriptive Sensitivity Analysis

Matthew Jackson
preprint en

Abstract

Fairness audits require choices about groups, reference populations, outcome definitions, and decision thresholds. This unreviewed technical preprint presents Libra, a prototype that stores some of those choices with process metadata and applies a selected threshold and reference group in a group-outcome audit. It reviews implementation boundaries and reports descriptive sensitivity analyses using one 2024 Michigan HMDA lender extract and a historical ProPublica COMPAS extract. The HMDA Black-to-White origination ratio is 0.8174 across retained action codes and 0.7162 for originated-versus-denied records under the stated filters; these are different administrative-outcome estimands, not AI-model performance measures. The paper uses no community participant data, validates no community-defined standard, and establishes no government endorsement or adoption. The public companion package documents source acquisition, scripts, results, and their limitations.

Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
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Libra: A Prototype for Provenance-Aware Fairness Audit Parameters — Implementation Review and Descriptive Sensitivity Analysis — Matthew Jackson · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS