My research interests revolve around the study of algorithmic systems and their evaluation, and how they reflect different values and priorities, and their impacts. My dissertation research examines evaluation practices, conceptions of risk, and impacts related to the use of new data analytics and machine learning tools in supply chain risk management. I mainly use qualitative methods informed by sensibilities from science and technology studies, infrastructure studies, and technical expertise in computer science. I have published articles examining impacts and other sociotechnical aspects of algorithmic systems such as on: unrest prediction based on social media data, harassment of women on social media, unemployed profiling in public welfare, and emotion recognition on social media. My work draws mainly upon literature from science and technology studies, sociology of risk & testing, organizational studies, algorithm & data studies, social computing, and infrastructure studies. My prior computer science education at the Vienna University of Technology was focused on the areas of natural language processing, parallel & distributed computing, machine learning, logic programming, and compiler construction.
I am currently a PhD candidate at the University of Michigan and advised by Christian Sandvig and Silvia Lindtner. I am affiliated with the Center for Ethics, Society, and Computing (ESC), the Infrastructure Lab and the Tech.Culture.Matters. research collective. I have also previously conducted research at the Austrian Academy of Sciences, National Institute of Informatics in Tokyo and Vienna University of Technology.
My work has been published in venues such as ACM SIGCHI (Human-Computer Interaction), ACM FAccT (Fairness, Accountability, and Transparency), ACM CSCW (Computer Supported Cooperative Work & Social Computing), and Frontiers in Big Data, and presented at the Society for the Social Studies of Science (4S), Association of Internet Researchers (AoIR), Society for the Advancement of Socio-Economics (SASE), European Association for the Study of Science and Technology (EASST), European Conference on Computer Supported Cooperative Work (ECSCW), Surveillance & Society conference, and the Data Justice conference.
|Mar 15, 2023||Presentation at STS Conference Graz 2023: How to teach the study of algorithms: Experiences from the field|
|Feb 20, 2023||Article accepted to CHI 2023: Online Harassment in Majority Contexts: Examining Harms and Remedies across Countries|
|Dec 29, 2022||Presentation at Data (Re)Makes the World conference of Information Society Project at Yale Law School: Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future|
|Aug 29, 2022||Presentation at 4s/ESOCITE Joint Meeting 2022: Making algorithms work: On the production of ignorance in the construction of accuracy measures in machine learning|
|Jul 30, 2022||Invited Talk at Digital Age Research Center at University of Klagenfurt: Constructing Certainty in Machine Learning: On the performativity of accuracy and its hold on the future|
|Mar 20, 2022||Presentation at EASST 2022: Constructing Certainty: The performativity of benchmarking and its hold on the future|
|Mar 11, 2022||Article accepted to CSCW 2022: Women’s Perspectives on Harm and Justice after Online Harassment|
|Jan 7, 2022||Article accepted to CSCW 2022! Preprint: Attitudes and Folk Theories of Data Subjects on Transparency and Accuracy in Emotion Recognition|
|Jul 27, 2021||Paper published in the CSCW Journal! Preprint: Future protest made risky: Examining social media based civil unrest prediction research and products|
|Oct 5, 2020||Presentation at AoIR 2020 on my research on protest prediction tools that use social media data. Extended Abstract available here.|
|Feb 21, 2020||Article published in Frontiers in Big Data: Algorithmic profiling of job seekers in Austria: how austerity politics are made effective|