Trustworthy AI
  • Documentation
    • Human oversight
    • Privacy and data governance
    • Technical robustness and security
    • Transparency and explicability
    • Diversity, non discrimination and fairness
    • Environmental and societal well-being
    • Accountability
    • Bibliography
  • Page
    • Bibliography
    • General links
    • AI System audit
    • Fairness Python libraries
    • Interpretability & transparency
    • ML Security & Robustness
    • Environmental impact
  • « Accountability

Bibliography¶

General links¶

  • European Union ethical guidelines

AI System audit¶

  • ISACA’s COBIT (2019)

  • European practical audit implementation

  • Python hashing libraries for obfuscation

  • Python library for data quality enforcement: great expectations

Fairness Python libraries¶

  • Tutorial: breaking myths about AI fairness

  • AIF 360

  • Fairlearn

  • Aequitas

Interpretability & transparency¶

  • Shapash

  • Shap

  • Shap values interpretation

  • LIME

  • [FR] Transparence et explicabilité des algorithmes, la grande confusion

ML Security & Robustness¶

  • MLFlow

  • Various attacks (H2OAI)

Environmental impact¶

  • [FR] IA durable et sobriété numérique : ce que les professionnels de la donnée peuvent faire

  • MLCO2: compute CO2 impact

  • Green algorithms: compute CO2 impact

  • Low resource ML

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