Towards Sustainability of AI

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Comply with Legal Frameworks and Company Policies new ​

Overview ​

Sustainability DimensionGovernance
ML Development PhaseML Demand Specification
ML Development StakeholdersBusiness Stakeholder, Domain Expert, Auditing & Testing

Description ​

The DP “Comply with Legal Frameworks and Company Policies” emphasizes the importance of an early evaluation of legal frameworks and company policies. Generally, regulation and transparency needs increase the governance requirements of ML projects, which leads to higher costs and potential legal or ethical issues (Laato et al., 2022). Thus, gathering information and examining applicable laws and regulations for the ML use case is an essential first step (Gill et al., 2022). Establishing an ML governance team as a corporate ethical review board that oversees all ML projects within an organization is advisable to facilitate knowledge sharing (Floridi et al., 2018).

Sources ​

  • 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
  • Gill, N., Mathur, A., & Conde, M. V. (2022). A Brief Overview of AI Governance for Responsible Machine Learning Systems. Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022. https://doi.org/10.48550/arXiv.2211.13130
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5