Artificial Intelligence Governance
Artificial intelligence governance aims to establish responsible development and deployment of AI systems, mitigating potential harms while maximizing benefits. Current research emphasizes data-centric approaches, moving beyond solely focusing on large "foundation" models to encompass the role of datasets in shaping AI capabilities and risks, and exploring various model classification frameworks to tailor governance strategies. This field is crucial for ensuring AI's ethical and equitable use across sectors, impacting not only scientific practices but also legal frameworks, industry standards, and public policy.
Papers
Auditing of AI: Legal, Ethical and Technical Approaches
Jakob Mokander
The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems
Jakob Mokander, Margi Sheth, David Watson, Luciano Floridi
Challenges and Best Practices in Corporate AI Governance:Lessons from the Biopharmaceutical Industry
Jakob Mökander, Margi Sheth, Mimmi Gersbro-Sundler, Peder Blomgren, Luciano Floridi