CITATION.cff

cff-version: 1.2.0
message: "If you use this software in your research, please cite it using the metadata below."
type: software
title: "Responsible-AI-evaluation"
abstract: >
  Open-source framework for stress-testing AI systems across bias, toxicity,
  truthfulness, robustness, and adversarial risk dimensions. Built for
  reproducibility, grounded in academic research, and designed for real-world
  governance, risk, and safety use cases aligned with the EU AI Act and
  NIST AI Risk Management Framework.
authors:
  - family-names: "Lago"
    given-names: "Felipe"
    alias: "felipelago17"
repository-code: "https://github.com/felipelago17/Responsible-AI-evaluation"
url: "https://felipelago17.github.io/Responsible-AI-evaluation/"
license: MIT
version: "1.0.0"
date-released: "2026-03-27"
keywords:
  - responsible-ai
  - llm-evaluation
  - ai-safety
  - bias-detection
  - eu-ai-act
  - governance
  - benchmarks
  - toxicity
  - truthfulness
  - robustness
  - llm
references:
  - type: article
    title: "TruthfulQA: Measuring How Models Mimic Human Falsehoods"
    authors:
      - family-names: "Lin"
        given-names: "Stephanie"
      - family-names: "Hilton"
        given-names: "Jacob"
      - family-names: "Evans"
        given-names: "Owain"
    year: 2022
    url: "https://arxiv.org/abs/2109.07958"
  - type: report
    title: "Artificial Intelligence Risk Management Framework (AI RMF 1.0)"
    authors:
      - name: "National Institute of Standards and Technology"
    year: 2023
    url: "https://doi.org/10.6028/NIST.AI.100-1"
  - type: article
    title: "BBQ: A Hand-Built Bias Benchmark for Question Answering"
    authors:
      - family-names: "Parrish"
        given-names: "Alicia"
      - family-names: "Chen"
        given-names: "Angelica"
      - family-names: "Nangia"
        given-names: "Nikita"
    year: 2022
    url: "https://arxiv.org/abs/2110.08193"