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?

19 Oct ’23
Explained – agreed. On the consequences of informed consent on explainability
Claus Beisbart
University of Bern
16 Nov ’23
Verification of Neural Networks — And What It Might Have to Do With Explainability
Computer Science
Daniel Neider
Technische Universität Dortmund
14 Dec ’23
A Legal Perspective on
Explainable AI: Why, How Much and For Whom?

Anne Lauber-Rönsberg
Technische Universität Dresden
18 Jan ’23
Organizing AI: How to
shape accountable AI development and use

Gudela Grote
ETH Zurich

Next lecture: Gudela Grote: Organizing AI: How to shape accountable AI development and use

18 Jan ’23 – 6.15pm

Online at: https://tu-dortmund.zoom.us/j/93719513109?pwd=cG9zaE1SQzBodDNyNjF5S1FRRU0zdz09 //  Meeting ID: 937 1951 3109 // Passcode: 275611

To register, send an e-mail with the title “Registration” to sara.mann@tu-dortmund.de. Include which lecture(s) you would like to attend and whether you will attend 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, Eva Schmidt

Funded by the