AIBias: Longitudinal Bias Auditing for Sequential Decision Systems
Provides tools for detecting, quantifying, and visualizing
algorithmic bias as a longitudinal process in repeated decision systems.
Existing fairness metrics treat bias as a single-period snapshot; this
package operationalizes the view that bias in sequential systems must be
measured over time. Implements group-specific decision-rate trajectories,
standardized disparity measures analogous to the standardized mean
difference (Cohen, 1988, ISBN:0-8058-0283-5), cumulative bias burden,
Markov-based transition disparity (recovery and retention gaps), and a
dynamic amplification index that quantifies whether prior decisions
compound current group inequality. The amplification framework extends
longitudinal causal inference ideas from Robins (1986)
<doi:10.1016/0270-0255(86)90088-6> and the sequential decision-process
perspective in the fairness literature (see <https://fairmlbook.org>)
to the audit setting. Covariate-adjusted trajectories are estimated via
logistic regression, generalized additive models (Wood, 2017,
<doi:10.1201/9781315370279>), or generalized linear mixed models
(Bates, 2015, <doi:10.18637/jss.v067.i01>). Uncertainty quantification
uses the cluster bootstrap (Cameron, 2008, <doi:10.1162/rest.90.3.414>).
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr (≥ 1.1.0), tidyr (≥ 1.3.0), ggplot2 (≥ 3.4.0), rlang (≥ 1.1.0), cli (≥ 3.6.0), purrr (≥ 1.0.0), tibble (≥ 3.2.0) |
| Suggests: |
mgcv, lme4, boot, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-04-04 |
| DOI: |
10.32614/CRAN.package.AIBias (may not be active yet) |
| Author: |
Subir Hait  [aut,
cre] |
| Maintainer: |
Subir Hait <haitsubi at msu.edu> |
| BugReports: |
https://github.com/causalfragility-lab/AIBias/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/causalfragility-lab/AIBias |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
AIBias results |
Documentation:
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