AIGovernance

Statistical Auditing and Governance Reporting for Employment AI Systems

Disclaimer: AIGovernance provides statistical auditing and documentation support tools only. It does not provide legal advice and does not certify compliance with any law or regulation.

Motivation

Organisations deploying AI systems in employment decisions face growing regulatory requirements — from the EEOC 4/5ths adverse impact rule to NYC Local Law 144 (mandatory annual bias audits for AEDTs) to the EU AI Act (High Risk classification for employment AI). Yet no dedicated R package provides a unified statistical workflow for these governance tasks.

AIGovernance fills this gap as a focused MVP for the employment domain.

Frameworks Covered (v0.1.0)

Framework	Jurisdiction	Coverage
EEOC Uniform Guidelines	US Federal	Adverse impact (4/5ths rule), Fisher & Z tests
NYC Local Law 144	New York City	Impact ratio table, disclosure format, procedural checklist
NIST AI RMF 1.0	US (voluntary)	GOVERN / MAP / MEASURE / MANAGE scoring
EU AI Act	European Union	Risk tier classification, Annex III, key obligations

Installation

# From GitHub (development version)
remotes::install_github("causalfragility-lab/AIGovernance")

# From CRAN (once accepted)
install.packages("AIGovernance")

Quick Start

library(AIGovernance)

# --- 1. Built-in synthetic hiring data ---
data(hiring_sim)

# --- 2. Build governance object ---
gov <- aigov_build(
  data        = hiring_sim,
  outcome     = selected,
  group       = race_ethnicity,
  ref_group   = "White",
  frameworks  = c("EEOC", "NYC_LL144", "NIST_RMF"),
  org_name    = "Acme Corporation",
  system_name = "ResumeAI v1.0"
)

# --- 3. Check applicable laws ---
aigov_scope(gov, domain = "employment", us_state = "NY")

# --- 4. EEOC adverse impact ---
gov <- aigov_adverse_impact(gov)

# --- 5. NYC Local Law 144 ---
gov <- aigov_audit_nyc(gov)

# --- 6. NIST AI RMF ---
gov <- aigov_audit_nist(gov, responses = list(
  GOVERN_1_1 = TRUE, MAP_1_1 = TRUE, MEASURE_1_1 = TRUE
))

# --- 7. EU AI Act risk classification ---
gov <- aigov_classify(gov, domain = "employment",
                       makes_final_decision = TRUE,
                       human_oversight = FALSE)

# --- 8. Generate HTML report ---
aigov_report(gov, format = "html")

Core Functions

Function	Description
`aigov_build()`	Construct governance audit object
`aigov_scope()`	Determine applicable frameworks by domain + jurisdiction
`aigov_adverse_impact()`	EEOC 4/5ths rule + statistical tests
`aigov_audit_nyc()`	NYC Local Law 144 impact ratios + checklist
`aigov_audit_nist()`	NIST AI RMF 1.0 GOVERN/MAP/MEASURE/MANAGE scoring
`aigov_classify()`	EU AI Act risk tier + NIST risk tier
`aigov_checklist()`	Display checklist items for any framework
`aigov_report()`	Generate HTML audit report

Ecosystem

AIGovernance is designed to work alongside:

Package	Role
`decisionpaths`	Longitudinal decision path construction
`DecisionDrift`	Temporal drift detection in repeated AI decisions
`AIBias`	Longitudinal bias amplification analysis

Planned for v0.2.0

aigov_monitor() — threshold-based drift alerts
aigov_remediate() — actionable remediation suggestions
Colorado AI Act (SB 205) module
Illinois AI Video Interview Act module
Integration with AIBias and DecisionDrift objects

References

EEOC (1978). Uniform Guidelines on Employee Selection Procedures. Federal Register, 43(166), 38295–38309.
New York City Local Law 144 (2021, effective 2023). https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page
NIST (2023). AI Risk Management Framework (AI RMF 1.0). https://doi.org/10.6028/NIST.AI.100-1
European Parliament and Council (2024). Regulation (EU) 2024/1689 (EU AI Act). https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

Citation

@Manual{Hait2026AIGovernance,
  title  = {AIGovernance: Statistical Auditing and Governance Reporting
             for Employment AI Systems},
  author = {Hait, Subir},
  year   = {2026},
  note   = {R package version 0.1.0},
  url    = {https://github.com/causalfragility-lab/AIGovernance}
}

Author

Subir Hait
Michigan State University
haitsubi@msu.edu
ORCID: 0009-0004-9871-9677