Explainable AI and Society

an interdisciplinary, hybrid lecture series

Modern AI can be used to drive cars, to decide on loans, or to detect cancer. Yet, the inner workings of many AI systems remain hidden – even to experts. Given the crucial role that AI systems play in modern society, this seems unacceptable. But how can we make complex, self-learning systems explainable? What kinds of explanations are we looking for when demanding that AI must be explainable? And which societal, ethical and legal desiderata can actually be satisfied via explainability?

20 Oct ’22
6:00 pm
What AI can Learn from Law
Réka Markovich
University of Luxembourg
17 Nov ’22
6:00 pm
Hybrid, Explanatory, Interactive Machine Learning – Towards Human-AI Partnership
Computer Science
Ute Schmid
University of Bamberg
15 Dec ’22
6:00 pm
What Advertising Data Tells us about Society
Computer Science
Ingmar Weber
Saarland University
19 Jan ’23
6:00 pm
Minimal Ethics – A Framework for Applied Ethics in the Digital Sphere
Vincent Müller
Friedrich-Alexander-University Erlangen-Nuremberg

Next lecture: Vincent Müller – Minimal Ethics

19 Jan’23 – 6:00 pm

Location: H 24 (RW I)

Further information will follow soon.​

To register, either click on the registration button and fill out the Google form or send an email with the subject “Registration” to Leon Weiser: leon.weiser@uni-bayreuth.de.

The mail should contain the dates you want to participate and whether you want to participate online or in person.


Click here for information on past lectures.

Scientific organizers

Kevin Baum, Georg Borges, Holger Herrmanns, Lena Kästner, Markus Langer, Astrid Schomäcker, Andreas Sesing, Ulla Wessels, Timo Speith

Funded by the