Sociotechnical Safety Evaluation of AI systems

Generative AI enables new use cases and modes of human-AI-interaction. These create ethical, social and safety risks which must be assessed in order to be managed or mitigated. However, current approaches to AI safety evaluation may miss relevant hazards due to not taking into account all relevant context, such as who uses the system and to what end. In this talk, I introduce a sociotechnical framework to AI safety evaluation that aims to capture relevant complexity, providing a holistic approach to AI safety evaluation. I canvass the current state of AI safety evaluation and point out key gaps. To close these gaps, I discuss possibilities for the field to expand beyond current evaluation methods and point out open challenges such as accuracy/ cost trade-offs, and representativeness and consent in the context of user studies and simulations. I close by highlighting ways toward implementing a sociotechnical approach to safety evaluation.

Bio: Laura Weidinger is a Staff Research Scientist at Google DeepMind, where she leads research on novel approaches to ethics and safety evaluation. Laura’s work focuses on taxonomising, evaluating, and mitigating risks from generative AI systems. Previously, Laura worked in cognitive science research and as policy advisor at UK and EU levels. She holds degrees from Humboldt Universität Berlin and University of Cambridge.