The increased use of computer systems to automate decision making in ethically sensitive domains, such as medicine and the judicial system, has sparked debates in public media, policy making and technical AI disciplines over the “ethics of algorithmic decision making”. These debates are often framed in terms of philosophically loaded terms such as ‘explainability’, ‘transparency’, ‘bias’ or ‘fairness’. This workshop brings together philosophers working on how these concepts should be understood in the context of algorithmic decision making to discuss the newest thinking and how philosophical analysis can contribute to these debates.
The workshop will take place in The Adrian House Seminar Room, Burrell's Field, Trinity College, Grange Road, Cambridge CB3 9DJ
09:45 What can political philosophy teach us about ‘fair’ machine learning? Reuben Binns (Oxford)
11:00 Bias in prediction vs. Bias in use David Danks (Carnegie Mellon)
13:30 A right to explanation, meaningful ex post explanation, and the grasp-ability test Tae Wan Kim (Carnegie Mellon)
14:45 Artificial Intelligent Systems: Trust and Understanding Eva Schmidt (Zürich)
16:30 Explainable AI – What is it, and What’s the Problem? Rune Nyrup (Cambridge)