Congratulations to Assistant Professor of Philosophy Ting-an Lin, whose two new course proposals, “Markets, Ethics, and Society” and “Ethics of Technology and Engineering,” were recently awarded the new Common Curriculum Grant. This grant supplies funding to faculty members who are creating courses that will be implemented into the new Common Curriculum for Leadership and Global Citizenship.

 

The Common Curriculum program, slated for a Fall 2025 introduction, provides students with a foundation for future learning. Ensuring that all students at the University of Connecticut are literate in many different types of knowledge, the Common Curriculum allows for students to enter society with a strong sense of moral and social responsibility.

Assistant Professor of Philosophy Ting-an Lin will be giving a public lecture a the Academic Sinica in Taiwan this Friday, December 13th. This talk is a part of their Beyond Gender: Diversity, Plurality, and Philosophy series. Professor Lin will also be joined by Assistant Professor Zhen-Rong Gan from Tunghai University and Hsiang-Yun Chen from Academia Sinica; they will be acting as the discussant and the moderator respectively.

 

Professor Ting-an Lin will be presenting her paper “AI, Normality, and Oppressive Things,” and you can read the abstract below:

While it is well-known that AI systems can be perniciously biased, much attention has been paid to instances where these biases are expressed blatantly. In this talk, I draw on the literature on the political impacts of artifacts to argue that many AI systems are not merely biased but materialize oppression. In other words, many AI systems should be recognized as oppressive things when they function to calcify oppressive normality, which treats the dominant groups as normal, whereas others as deviations. Adopting this framework emphasizes the crucial roles that physical components play in sustaining oppression and helps identify instances of AI systems that are oppressive in a subtler way. Using instances of generative AI systems as the central examples, I theorize three ways that AI systems might function to calcify oppressive normality—through their content, their performance, and their style. Since the oppressiveness of oppressive things is a matter of degree, I further analyze three contributing factors that make the oppressive impacts of AI systems especially concerning. I end by discussing the limitations of existing measures and urge the exploration of more transformative remedies.

 

Congratulations, Ting!