Sense-Making of Machine Learning by Non-ML experts
Part of Project IKON
To understand how stakeholders would make sense of the proposed prototype, we developed a method for conducting co-design workshops with actual ML technologies. Designing bespoke transparencies that represented possible explanations (see left), participants engaged in a series of open tasks. We studied the patterns of behavior with the supplied artefacts, and gained invaluable insights for the further progression of our prototype regarding socio-technical factors that could not have been appreciated before.
Benjamin, Jesse Josua, Christoph Kinkeldey, Claudia Müller-Birn, Tim Korjakow, and Eva-Maria Herbst. 2021. ‘Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability’. Accepted to ACM GROUP 2022. ArXiv:2109.11849 [Cs]. http://arxiv.org/abs/2109.11849.