As promising as machine learning techniques are in terms of their potential to do good, the technologies raise a number of ethical questions and are prone to biases that can subvert their well-intentioned goals.
Machine learning systems, from simple spam filtering or recommender systems to Deep Learning and AI, have already arrived at many different parts of society. Which web search results, job offers, product ads and social media posts we see online, even what we pay for food, mobility or insurance - all these decisions are already being made or supported by algorithms, many of which rely on statistical and machine learning methods. As they permeate society more and more, we also discover the real world impact of these systems due to inherent biases they carry. For instance, criminal risk scoring to determine bail for defendants in US district courts has been found to be biased against black people, and analysis of word embeddings has been shown to reaffirm gender stereotypes due to biased training data. While a general consensus seems to exist that such biases are almost inevitable, solutions range from embracing the bias as a factual representation of an unfair society to mathematical approaches trying to determine and combat bias in machine learning training data and the resulting algorithms. Besides producing biased results, many machine learning methods and applications raise complex ethical questions. Should governments use such methods to determine the trustworthiness of their citizens? Should the use of systems known to have biases be tolerated to benefit some while disadvantaging others? Is it ethical to develop AI technologies that might soon replace many jobs currently performed by humans? And how do we keep AI and automation technologies from widening society's divides, such as the digital divide or income inequality?
Panel Discussion + Keynotes
This event provides a platform for multidisciplinary debate in the form of keynotes and a panel discussion with international experts from diverse fields:
- Prof. Moshe Vardi: "Deep Learning and the Crisis of Trust in Computing"
- Prof. Sarah Spiekermann-Hoff: “The Big Data Illusion and its Impact on Flourishing with General AI”
Panelists: Ethics and Bias in AI
- Prof. Moshe Vardi, Karen Ostrum George Distinguished Service Professor in Computational Engineering, Rice University
- Prof. Peter Purgathofer, Centre for Informatics and Society / Institute for Visual Computing & Human-Centered Technology, TU Wien
- Prof. Sarah Spiekermann-Hoff, Institute for Management Information Systems, WU Vienna
- Prof. Mark Coeckelbergh, Professor of Philosophy of Media and Technology, Department of Philosophy, University of Vienna
- Dr. Christof Tschohl, Scientific Director at Research Institute AG & Co KG
Moderator: Markus Mooslechner, Terra Mater Factual Studios
The evening will be complemented by networking & discussions over snacks and drinks.