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
Roxygen: list(markdown = TRUE)
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
Config/pak/sysreqs: libicu-dev
Repository: https://causalfragility-lab.r-universe.dev
Date/Publication: 2026-04-04 19:12:05 UTC
RemoteUrl: https://github.com/causalfragility-lab/aibias
RemoteRef: HEAD
RemoteSha: 43d6fce404e76ff2ae4a6f978f17f03ee9b66cf5
NeedsCompilation: no
Packaged: 2026-07-05 09:36:53 UTC; root
Author: Subir Hait [aut, cre] (ORCID: <https://orcid.org/0009-0004-9871-9677>)
Maintainer: Subir Hait <haitsubi@msu.edu>