{ "_id": "6a22ac150ef520ea2f58bdb1", "Package": "AIBias", "Title": "Longitudinal Bias Auditing for Sequential Decision Systems", "Version": "0.1.1", "Authors@R": "person(\"Subir\", \"Hait\",\nemail = \"haitsubi@msu.edu\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0004-9871-9677\"))", "Description": "Provides tools for detecting, quantifying, and visualizing\nalgorithmic bias as a longitudinal process in repeated decision\nsystems. Existing fairness metrics treat bias as a\nsingle-period snapshot; this package operationalizes the view\nthat bias in sequential systems must be measured over time.\nImplements group-specific decision-rate trajectories,\nstandardized disparity measures analogous to the standardized\nmean difference (Cohen, 1988, ISBN:0-8058-0283-5), cumulative\nbias burden, Markov-based transition disparity (recovery and\nretention gaps), and a dynamic amplification index that\nquantifies whether prior decisions compound current group\ninequality. The amplification framework extends longitudinal\ncausal inference ideas from Robins (1986)\n and the sequential\ndecision-process perspective in the fairness literature (see\n) to the audit setting.\nCovariate-adjusted trajectories are estimated via logistic\nregression, generalized additive models (Wood, 2017,\n), or generalized linear mixed\nmodels (Bates, 2015, ). Uncertainty\nquantification uses the cluster bootstrap (Cameron, 2008,\n).", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-US", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "LazyData": "true", "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": { "Date": "2026-06-05 10:55:32 UTC", "User": "root" }, "Author": "Subir Hait [aut, cre] (ORCID: )", "Maintainer": "Subir Hait ", "MD5sum": "aa636214dba65b4622bc5622ee6580a4", "_user": "causalfragility-lab", "_type": "src", "_file": "AIBias_0.1.1.tar.gz", 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