Anne C. Kroon (PhD, 2017, University of Amsterdam) is an associate professor of Communication, Organisations and Society at the Department of Communication Science at the University of Amsterdam.

Research

My research examines bias in digital media and methods using computational techniques and experiments. I analyze depictions of social groups and their impact on prejudice and discrimination. Supported by an NWO Veni grant, I investigate how digital technologies influence biases in hiring and recruitment, focusing on implicit biases in algorithms and unconscious biases of recruiters. I am particularly interested in the consequences of algorithmic profiling for vulnerable job seekers, such as older individuals, in the digital job market. 

Additionally, I am exploring the content, causes, and effects of bias in computational tools, supported by an NWO Open Competition grant. Specifically, I am interested in the degree to which annotation bias contributes to bias in machine learning models and its downstream implications for classification. 

Impact: Digital Infrastructure and Data Science

As an active member of various infrastructure and data science initiatives, my primary goal is to accelerate computational and data science research for SSH-researchers. I previously contributed to the PDI-SSH funded project Twi-XL, which creates an infrastructure for cross-media research, and the ODISSEI-funded Media and Content Analysis Lab (MCAL), which integrates content analytical tools and communication science data into the broader SSH infrastructure landscape. I currently serve as the communication science representative of the Social and Behavioral Science Data Science Center of the University of Amsterdam. 

Affiliations

I am affiliated with ASCoR, and part of the Program Group Communication, Organisations and Society. In addition, I am a member of Computational Communication Science Amsterdam, and I act as a supervisor and research member of the ERC project NEWSFLOWS. I am also involved in the EU-funded project RESPOND, where I contribute to a funded work package that examines the role of media in corruption using computational methods. I am also involved in the EU-funded project RESPOND, where I contribute to a funded work package that examines the role of media in corruption using computational methods.

I specialize in teaching and supervising computational social science techniques. I am passionate about sharing knowledge on big data and digital methods through seminars and courses for both undergraduate and graduate students. As a co-supervisor of several PhD candidates, I have experience guiding research projects where computational methods play a central role.