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The deployment of artificial intelligence (AI) holds great promise for the economy and society. However, it also brings forth risks concerning social equality, privacy, fairness, and accountability. Recent studies have shed light on significant race and gender biases in algorithmic systems, revealing how AI can negatively impact marginalized groups. Surprisingly, the category of age, crucial for social inclusion and equality in aging societies, has been largely overlooked in research and policy on AI bias.

This project addresses related questions about inequality in labor markets resulting from gender, race, and family status in the United States and Europe. Firstly, I analyze (with Profs. Michelle J. Budig and Melissa Hodges) racial and gender differences in the effects of educational attainment on wages in the United States. Importantly, we look at various education specifications and use regression decomposition and wage return models with race and gender interactions to explore group differences in educational attainment and its impact on pay gaps. Group differences in educational attainment explain some of the race pay gap, but none of the gender pay gap.

What is the role of families and schools in the creation of educational inequalities? Are schools a “great equalizer” (Mann, 1848), providing opportunities for children from socioeconomically disadvantaged families? Or, conversely, do schools amplify not opportunity but inequality, because children from socioeconomically disadvantaged families attend disadvantaged schools in terms of facilities, teachers, and peers, while children from better-off families attend high-quality schools?

Political representation is a central pillar of modern democracies. Well-functioning representation is essential for trust between voters and political parties. However, the trust that political parties act in the interest of their voters is repeatedly challenged - not least by populist forces.

Human coding of large text corpora is an important, but very labor intensive and time-consuming pillar of political science research. Consequently, the rise and constant improvement of automated classification models presents itself as a perfect possibility to alleviate this burden, by enabling researchers to execute large coding tasks within a fraction of the time and work typically required. However, the reliability, consistency and quality of automated coding remains a concern. Can the computer really take over from humans and how comparable will the results be?

My dissertation examines how the regulatory environment of abortion and medically assisted reproduction (MAR) shapes the practices and experiences of multifetal pregnancy reduction (MFPR), a procedure to eliminate one or more fetal lives in multiple pregnancies. As the incidence of multiple pregnancy is significantly higher in MAR-induced pregnancies, MFPR is usually performed in the context of MAR, in which people intend to initiate pregnancy and achieve live birth; however, the procedure involves, at least partially, voluntary elimination of a fetal life.

The project "Visions of the Post-Viral Society: Changes in Political Perceptions in the Course of the Covid19-Crisis" investigates whether new forms and sources of political legitimacy emerged during and as a result of the Covid19 pandemic, in particular as a consequence of the strong political intervention in public life and the economy. Machine learning algorithms will be used to analyze the evolution of political discourse on social media before, during and after the crisis in order to identify new and changing political demands and interests of citizens active on these platforms.

The doctoral project examines digitization in the skilled crafts sector. Case studies in craft enterprises are used to investigate the question of how craft enterprises implement digital technologies and what effects this has on the work in the enterprises and the employees. In addition to the case studies, expert interviews will be conducted with advisors from craft organizations, IT service providers and machine manufacturers.

“For to every one who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away.”

— Matthew 25:29, RSV.