# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "AIBias" in publications use:' type: software license: MIT title: 'AIBias: Longitudinal Bias Auditing for Sequential Decision Systems' version: 0.1.1 doi: 10.32614/CRAN.package.AIBias abstract: 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) and the sequential decision-process perspective in the fairness literature (see ) to the audit setting. Covariate-adjusted trajectories are estimated via logistic regression, generalized additive models (Wood, 2017, ), or generalized linear mixed models (Bates, 2015, ). Uncertainty quantification uses the cluster bootstrap (Cameron, 2008, ). authors: - family-names: Hait given-names: Subir email: haitsubi@msu.edu orcid: https://orcid.org/0009-0004-9871-9677 repository: https://causalfragility-lab.r-universe.dev repository-code: https://github.com/causalfragility-lab/AIBias commit: 43d6fce404e76ff2ae4a6f978f17f03ee9b66cf5 url: https://github.com/causalfragility-lab/AIBias date-released: '2026-04-04' contact: - family-names: Hait given-names: Subir email: haitsubi@msu.edu orcid: https://orcid.org/0009-0004-9871-9677