Audit, Don’t Explain – Recommendations Based on a Socio-Technical Understanding of ML-Based Systems

Heuer, HendrikWienrich, CarolinWintersberger, PhilippWeyers, Benjamin2021-09-052021-09-052021https://dl.gi.de/handle/20.500.12116/37371In 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 Wartentest can ensure that ML systems operate in the interest of the public.enAlgorithmic BiasAlgorithmic ExperienceAlgorithmic TransparencyHuman-Centered Machine LearningRecommender SystemsSocial MediaUser BeliefsAudit, Don’t Explain – Recommendations Based on a Socio-Technical Understanding of ML-Based SystemsText/Workshop Paper10.18420/muc2021-mci-ws02-232