Organizers: Moni Naor (Weizmann Institute of Science; chair), Nicole Immorlica (Microsoft Research), Steven Wu (University of Minnesota Twin Cities)
This workshop will take place at the end of the cluster. It will examine the causes and consequences of bias in machine learning. It will cover recent results on statistical and individual notions of fairness in algorithmic decisions and examine the circumstances for which they are achievable or provably impossible. In particular, it will also examine more complex settings where multiple decisions are made in parallel or in a pipelined manner and examine appropriate notions of fairness in these settings. The aim is to bring together diverse researchers interested in a rigorous approach to the issues.
All events take place in the Calvin Lab auditorium.
Further details about this workshop will be posted in due course. Enquiries may be sent to the organizers workshop-fairness2 [at] lists [dot] simons [dot] berkeley [dot] edu (at this address).
Registration is required to attend this workshop. Space may be limited, and you are advised to register early. The link to the registration form will appear on this page approximately 10 weeks before the workshop. To submit your name for consideration, please register and await confirmation of your acceptance before booking your travel.