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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