# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "AIGovernance" in publications use:'
type: software
license: MIT
title: 'AIGovernance: Statistical Auditing and Governance Reporting for Employment
  AI Systems'
version: 0.1.0
abstract: 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, <https://doi.org/10.6028/NIST.AI.100-1>). 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.
authors:
- family-names: Hait
  given-names: Subir
  email: haitsubi@msu.edu
  orcid: https://orcid.org/0009-0004-9871-9677
repository: https://cran.r-universe.dev
repository-code: https://github.com/causalfragility-lab/AIGovernance
commit: 3ed121f1d89d513e9cf80232b478ea064719a663
url: https://github.com/causalfragility-lab/AIGovernance
date-released: '2026-05-27'
contact:
- family-names: Hait
  given-names: Subir
  email: haitsubi@msu.edu
  orcid: https://orcid.org/0009-0004-9871-9677