Audit, Don't Explain -- Recommendations Based on a Socio-Technical Understanding of ML-Based Systems
arXiv:2107.09917 · doi:10.18420/muc2021-mci-ws02-232
Abstract
In this position paper, I provide a socio-technical perspective on machine learning-based systems. I also explain why systematic audits may be preferable to explainable AI systems. I make concrete recommendations for how institutions governed by public law akin to the German TÜV and Stiftung Warentest can ensure that ML systems operate in the interest of the public.
This paper will be presented at the Workshop on User-Centered Artificial Intelligence (UCAI '21) at Mensch und Computer 2021