Attention: Due to current circumstances, there are a severely limited number of seats at the event.
Please register in time here for CS Talk #8.

Abstract

Artificial intelligence, together with large amounts of data (Big Data), promises to enable previously unfeasible calculations, predictions and personalization. However, numerous examples of applications in medicine, recruiting or risk assessment indicate that AI systems can reproduce and intensify existing social inequalities and stereotypes, particularly with respect to race, gender or economic conditions. Such systems result in discrimination by AI technology (O’Neil 2016, Noble 2018, Benjamin 2019).
In my talk I will discuss case studies that illustrate such undesirable effects and analyze the underlying knowledge politics. I will, moreover, introduce to current approaches that aim to address and mitigate the discriminatory effects. Drawing on feminist science and technology studies (STS) I will critically reflect epistemological assumptions of AI endeavors, including criticism such as the intention of fair algorithms (by the computer science community) or ethic guidelines in companies and the EU. The main aim of my talk though is to unfold a theoretical understanding and practical engagement for a “response-able” (Haraway 2008, Barad 2012) socio-technical reconfiguration of AI.

Bio