Fairness, accountability and transparency in Machine Learning? Jo Bates reports back from ACM FAT* in Atlanta, USA
A couple of weeks ago I travelled to Atlanta, USA to attend ACM FAT* - an interdisciplinary conference that addresses issues of Fairness, Accountability and Transparency in Machine Learning. Officially, I was there on the hunt for potential papers and authors to invite to submit their work to Online Information Review . However, the FAT* field is also closely related to my research interests around the politics of data and algorithms, and my teaching on the Information School’s MSc Data Science . I was keen to check out what was happening in the FAT* community, and feed my findings back into my teaching and into two new projects I am working on in this field: CYCAT & supervising a new PhD student – Ruth Beresford – whose research will investigate algorithmic bias in collaboration with the Department for Work and Pensions. I was privileged to hear a number of great papers – the best of which engaged critically with issues of social context and justice. My two favourite pa...