Package: AIBias
Title: Longitudinal Bias Auditing for Sequential Decision Systems
Version: 0.1.1
Authors@R: 
    person("Subir", "Hait",
           email   = "haitsubi@msu.edu",
           role    = c("aut", "cre"),
           comment = c(ORCID = "0009-0004-9871-9677"))
Description: 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>).
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.3.3
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)
Config/testthat/edition: 3
VignetteBuilder: knitr
LazyData: true
Depends: R (>= 4.1.0)
URL: https://github.com/causalfragility-lab/AIBias
BugReports: https://github.com/causalfragility-lab/AIBias/issues
NeedsCompilation: no
Packaged: 2026-04-04 19:12:37 UTC; Subir
Author: Subir Hait [aut, cre] (ORCID: <https://orcid.org/0009-0004-9871-9677>)
Maintainer: Subir Hait <haitsubi@msu.edu>
Repository: CRAN
Date/Publication: 2026-04-06 22:30:16 UTC
Built: R 4.6.0; ; 2026-04-26 16:01:23 UTC; unix
