Statistical Auditing and Governance Reporting for Employment AI Systems

Provides statistical auditing, risk documentation, and reporting tools to support AI governance workflows for employment and hiring decision systems. Implements the EEOC four-fifths adverse impact rule (Equal Employment Opportunity Commission, 1978, < https://www.ecfr.gov/current/title-29/subtitle-B/chapter-XIV/part-1607>), NYC Local Law 144 bias audit requirements (New York City, 2023, < https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page>), and the AI Risk Management Framework checklist items from the National Institute of Standards and Technology (2023, ). Optionally supports EU AI Act high-risk classification (European Parliament and Council, 2024, < https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689>). The package does not provide legal advice or certify legal compliance; it is a statistical and documentation support tool.

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)

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

Ecosystem

AIGovernance is designed to work alongside:

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
[email protected]
ORCID: 0009-0004-9871-9677

License

MIT © Subir Hait