Data Feminism - Perspectives from Culture, Medicine & Computer Science

Location: Rubenowstr. 3, lecture hall*

*Exception: 3 June

Time: 6 p.m. c.t., please see below for exact data

Organisation: Dr. Jenny Linek

Countless data is generated every day, whether when shopping, researching online, travelling, using voice recognition software, in recruitment procedures, on social networks, in hospitals, or in insurance and banking matters. However, the majority of people affected by this have little knowledge of how and with what intentions this data is generated and exploited. The use of algorithmic computer programmes that make a selection based on this data and anticipate decisions is hardly questioned in everyday life, as it goes hand in hand with the promise of absolute objectivity and high efficiency. Diseases are supposed to be treated more effectively if large amounts of personal data are analysed in an automated way, and staffing procedures are supposed to be fairer if no human emotion or bias is involved. But how realistic are these promises? How much room is left for the critical examination of mathematical models if they are considered neutral entities per se?

In the past few years, books such as Weapons of Math Destruction (2016) by Cathy O'Neil, Invisible Women (2020) by Caroline Criado-Perez and Data Feminism (2020) by Catherine D'Ignazio and Lauren F. Klein have impressively demonstrated how important it is for gender studies to engage with the systematics of data. As the authors show, existing social inequalities in "a world that is increasingly based on data and increasingly dominated by data" (Criado-Perez) threaten to worsen massively if we do not take care to include feminist and intersectional approaches in the research and development of algorithms and apps. The numbers from software systems do not speak a clear and, above all, neutral language as long as the data on which they are based includes omissions, prejudices and biases that are reproduced by algorithms.

This year's IZfG lecture series addresses this point and brings together internal and external experts from the fields of data science, sociology, cultural studies, medical informatics, philosophy and medical ethics for the summer semester 2025 lecture series. The IZfG invites experts from a wide range of relevant subject areas to take part in this series, which in turn offers important impulses for students and researchers across disciplinary boundaries.

Accordingly, one of the central concerns of the lecture series will be to address questions such as the following: Which impetus can be provided by gender studies for digitalisation and subject areas with data-driven research? To what extent are biases and stereotyping becoming visible in the development of AI? How are these phenomena perceived and reflected upon in cultural studies? Which forms of cross-disciplinary cooperation are useful and enable the potential of big data and AI to be exploited equally by all?

Literature:

• Criado-Perez, Caroline: Unischtbare Frauen. Wie eine Daten beherrschte Welt die Hälfte der Bevölkerung ignoriert, Munich 2020.
• D'Ignazio, Catherine/ Klein, Lauren F.: Data Feminism. Cambridge and others 2020.
• O'Neil, Cathy: Weapons of Math Destruction. Big Data Increases Inequality and Threatens Democracy, New York 2016