How to anticipate risks in AI-enhanced research

Presenter:

Sarah Brown, Assistant Professor, Computer Science

ML-powered AI is increasingly appearing in all areas of research. However, ML (machine learning) is still in many ways a research output, more of a prototype than a polished, reliable, product. Even commercially released systems, because they are software, can be released without the same rigorous testing that commercially available research equipment has been traditionally be subject to. In this talk, I will draw connections between the ML research process and potential risks in applied ML and use of commercial AI in research.