Towards Sustainability of AI

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Engage in ML-Model Auditing new ​

Overview ​

Sustainability DimensionGovernance
ML Development PhaseDeployment and Monitoring
ML Development StakeholdersBusiness Stakeholder, Auditing & Testing

Description ​

The higher the risk for potentially harmful decisions, the more companies should engage in auditing ML models before deployment (Schulam & Saria, 2019). Therefore, “Engage in ML-Model Auditing“ describes the formal audit process of ML models. “Algorithm Auditing is the research and practice of assessing, mitigating, and assuring an algorithm’s safety, legality, and ethics” (Koshiyama et al., 2021, p. 2). To mitigate the risk of inadequate auditing, Laato et al. (2022) propose to connect the deployment testing directly to organizational audit goals, ensuring continuous auditing.

Sources ​

  • Schulam, P., & Saria, S. (2019). Can you trust this prediction? Auditing pointwise reliability after learning. The 22nd International Conference on Artificial Intelligence and Statistics, 1022–1031.
  • Koshiyama, A., Kazim, E., Treleaven, P., Rai, P., Szpruch, L., Pavey, G., Ahamat, G., Leutner, F., Goebel, R., Knight, A., Adams, J., Hitrova, C., Barnett, J., Nachev, P., Barber, D., Chamorro-Premuzic, T., Klemmer, K., Gregorovic, M., Khan, S., & Lomas, E. (2021). Towards Algorithm Auditing: A Survey on Managing Legal, Ethical and Technological Risks of AI, ML and Associated Algorithms. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3778998
  • Laato, S., Birkstedt, T., Mäantymäki, M., Minkkinen, M., & Mikkonen, T. (2022). AI governance in the system development life cycle: Insights on responsible machine learning engineering. Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, 113–123. https://doi.org/10.1145/3522664.3528598