Two computer scientists and a cultural scientist get hit by a driver-less car: a method for situating knowledge in the cross-disciplinary study of F-A-T in machine learning: translation tutorial.

In a workshop organized in December 2017 in Leiden, the Netherlands, a group of lawyers, computer scientists, artists, activists and social and cultural scientists collectively read a computer science paper about 'improving fairness'. This session was perceived by many participants as eye-opening on how different epistemologies shape approaches to the problem, method and solutions, thus enabling further cross-disciplinary discussions during the rest of the workshop. For many participants it was both refreshing and challenging, in equal measure, to understand how another discipline approached the problem of fairness. Now, as a follow-up we propose a translation tutorial that will engage participants at the FAT* conference in a similar exercise. We will invite participants to work in small groups reading excerpts of academic papers from different disciplinary perspectives on the same theme. We argue that most of us do not read outside our disciplines and thus are not familiar with how the same issues might be framed and addressed by our peers. Thus the purpose will be to have participants reflect on the different genealogies of knowledge in research, and how they erect walls, or generate opportunities for more productive inter-disciplinary work. We argue that addressing, through technical measures or otherwise, matters of ethics, bias and discrimination in AI/ML technologies in society is complicated by the different constructions of knowledge about what ethics (or bias or discrimination) means to different groups of practitioners. In the current academic structure, there are scarce resources to test, build on-or even discard-methods to talk across disciplinary lines. This tutorial is thus proposed to see if this particular method might work.