CITATION.cff

# Citation metadata for AICertify.
#
# GitHub reads this file and offers a "Cite this repository" button, and Zenodo
# reads it when archiving a release. The audience for this tool writes
# conformity assessments, policy submissions and papers, so being citable in a
# form they can paste is worth more than another badge.
cff-version: 1.2.0
message: "If you use AICertify in your research or compliance work, please cite it as below."
title: "AICertify: compliance-as-code evaluation for AI systems"
abstract: >-
  AICertify evaluates an AI system against regulatory policy written as Open
  Policy Agent Rego, and produces audit-ready reports in PDF, Markdown, JSON or
  HTML. It computes the measured half of a compliance picture, including
  fairness, toxicity and content-safety metrics, rather than accepting them as
  declarations, and evaluates the result against the GOPAL policy library
  covering the EU AI Act, the UK AI framework, NIST AI RMF and further
  sector-specific frameworks.
type: software
authors:
  - family-names: Madan
    given-names: Kapil
    affiliation: "Principled Evolution"
  - name: "Principled Evolution"
    website: "https://principledevolution.ai"
version: "0.8.1"
date-released: "2026-08-28"
license: Apache-2.0
repository-code: "https://github.com/Principled-Evolution/AICertify"
url: "https://github.com/Principled-Evolution/AICertify"
references:
  - type: software
    title: "GOPAL: the Rego policy library for AI compliance"
    doi: 10.5281/zenodo.22142302
    repository-code: "https://github.com/Principled-Evolution/gopal"
    authors:
      - family-names: Madan
        given-names: Kapil
      - name: "Principled Evolution"
keywords:
  - ai-governance
  - ai-compliance
  - eu-ai-act
  - nist-ai-rmf
  - policy-as-code
  - open-policy-agent
  - opa
  - rego
  - model-evaluation
  - responsible-ai