A Socio-Technical Perspective on the Potentials and Perils of Machine Learning

Abstract

Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This talk discusses what this difference means for the critical analysis of socio-technical systems based on machine learning. As a basis for this analysis, I provide a socio-technical perspective on machine learning-based systems. I engage with how the term machine learning is framed and constructed by practitioners. I also examine ways of studying user beliefs about ML-based systems. In addition to that, I explain why systematic audits may be preferable to explainable AI systems. Based on these insights, 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.


About the Speaker

Hendrik Heuer is a Researcher at the University of Bremen associated with the Institute for Information Management (ifib). His focus areas are Data Science and Digital Humanities. He studied Digital Media, Human-Computer Interaction, and Machine Learning in Bremen, Buffalo, Stockholm (KTH), Helsinki (Aalto) and Amsterdam (UvA).

Speaker:
Dr. Hendrik Heuer

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The Data Science Center is funded by: