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AIBias  

Longitudinal Bias Auditing for Sequential Decision Systems
View on CRAN: Click here


Download and install AIBias package within the R console
Install from CRAN:
install.packages("AIBias")

Install from Github:
library("remotes")
install_github("cran/AIBias")

Install by package version:
library("remotes")
install_version("AIBias", "0.1.1")



Attach the package and use:
library("AIBias")
Maintained by
Subir Hait
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-06
Latest Update: 2026-04-06
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>).
How to cite:
Subir Hait (2026). AIBias: Longitudinal Bias Auditing for Sequential Decision Systems. R package version 0.1.1, https://cran.r-project.org/web/packages/AIBias. Accessed 05 Jun. 2026.
Previous versions and publish date:
0.1.0 (2026-04-04 11:30)
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Complete documentation for AIBias
Functions, R codes and Examples using the AIBias R package
Full AIBias package functions and examples
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