Will Orr

Hello! My name is Will Orr. I am a PhD candidate at the Annenberg School of Communication where I study the sociotechnical dynamics of creating and evaluating Artificial Intelligence systems.

My research uncovers the histories, practices, and cultures of emerging AI technologies, to critically assess how these systems can be designed and deployed more responsibly. In particular, I study AI training datasets and evaluation frameworks as key sites through which AI systems interpret the world, shaping their development, deployment, and the politics they enact.

I am currently completing my dissertation, which explores how data practitioners enact and negotiate the concept of data quality in the creation of AI datasets. It uncovers the messy and contingent work of maintaining quality in the shifting landscape of contemporary AI and data science. My work primarily engages qualitative approaches, such as interviews, ethnographic, and archival methods, though I am also trained in data science and welcome opportunities to contribute to quantitative or mixed-methods research.

Prior to the PhD, I completed a Bachelor of Arts from the University of Melbourne, where I studied sociology, politics and international relations, and economics. I also earned an Honours in sociology, and a Master of Applied Data Analytics from the Australian National University (ANU).

I am a member of the Knowing Machines project, the Media As SocioTechnical Systems (MASTS) community, as well as the Justice and Technoscience (JusTech) Lab at ANU's School of Regulation and Global Governance (RegNet). My work has been published across academic journals and conferences, and has been featured in news outlets such as Australian Broadcasting Corporation Radio, Canberra Daily, and Nature.

Selected Publications

AI datasets and infrastructures

• Orr, W. (2025). Ghosts in the data: The contested politics of absence in data infrastructures. Social Science Computer Review.
• Orr, W., & Kang, E. B. (2024). AI as a sport: On the competitive epistemologies of benchmarking. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 1875–1884.
• Orr, W., & Crawford, K. (2024). The social construction of datasets: On the practices, processes, and challenges of dataset creation for machine learning. New Media & Society, 26(9), 4955–4972.
• Orr, W., & Crawford, K. (2024). Building Better Datasets: Seven recommendations for responsible design from dataset creators. Journal of Data-centric Machine Learning Research, 1(1), 1–21.

AI ethics and regulation

• Lee, A., Orr, W., Johnson, W. G., Harb, J. I., & Henne, K. (2023). Barriers to Regulating AI: Critical Observations from a Fractured Field. In S. Lindgren (Ed.), Handbook of Critical Studies of Artificial Intelligence. (pp. 413–423). Edward Elgar Publishers.
• Orr, W., & Davis, J. L. (2020). Attributions of ethical responsibility by Artificial Intelligence practitioners. Information, Communication & Society, 23(5), 719–735.

Marginalization and governance

• Orr, W. (2024). Counting on stability: The social construction of the Los Angeles homeless count. International Journal of Communication, 18, 808–815.
• Orr, W., Henne, K., Lee, A., Harb, J. I., & Alphonso, F. C. (2023). Necrocapitalism in the Gig Economy: The Case of Platform Food Couriers in Australia. Antipode, 55(1), 200–221.