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>).

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