23/10/2026
[Departmental Seminar]
Topics: AI Classification and Alterity: A Critique of Political and Ontological Bias
Speaker: Prof. Nahum Brown (Assistant Professor, The Mahidol University International College)
Date: 2 November 2026 (Mon)
Time: 4:30pm-6:30pm HK Time
Venue: Room 220, Fung King Hey Building (Limited seats. Registrations will be handled on a first come, first served basis.)
Face-to-face Only
Register by 1 November 2026
Link: https://cloud.itsc.cuhk.edu.hk/webform/view.php?id=13745281
Enquiries:
Tel: 3943 7135
Email: [email protected]
Abstract:
AI classification bias is one of the most pressing ethical issues of AI technologies today. Most of the literature on this topic focuses on cultural and political prejudices that undermine the “objectivity” of computer vision object recognition, such as racial, gender, and economic inequalities, while exploring strategies for how to redesign machine learning datasets to be relatively fairer. Complicating this trend, I argue that there is a distinction to be made between surface-level political biases and deep-rooted ontological assumptions. I argue that while the goal of surface-level objectivity is important, this goal should not obscure the ontological challenges we face. Proponents of the argument from alterity rebuke the optimism that establishing an “objectivity” of object recognition is possible at the ontological level. Drawing from contemporary sources about AI, this article offers a critique of AI classification bias by analyzing three strategies that aim to reduce political bias (the category strategy, the big data strategy, and the genealogy strategy) as well as three challenges that demonstrate the difficulties of mitigating ontological bias (the universal vision challenge, the predictability of the future, and the transparency assumption).
Delivered in English. All are welcome.