CETL
Understanding the Critique of AI
A growing body of scholarship has been interrogating AI and questioning the rush to adoption surrounding it, creating a whole new interdisciplinary subfield of scholarly inquiry. New journals have emerged, like Critical AI, as well as conferences, such as the “Critical AI Studies?” conference, sponsored by the Center for the Humanities and Machine Learning at the University of California, Santa Barbara. The lines of inquiry in this field go beyond merely warning about hallucinations and sycophancy. They point out the fundamentally unethical practices in the development of the LLM models, utilizing copyrighted works of writers and artists without their consent or compensation. They underscore the great cost to the mental health of those involved in training the algorithms. They interrogate what happens to our collective soul when we mischaracterize probabilistic computer output as intelligence and creativity. They question the long-term consequences of anthropomorphizing computer code. They chronicle the cost to the marginalized communities in which data centers are built, not to mention the environmental costs for all. They document the role of AI in powering the modern surveillance apparatus. They challenge the narrative that AI is simply an inevitability, and those who resist unquestioning adoption are luddites stalling progress. They ask why these facts are not conceptualized and taught as part of basic AI literacy.
This webpage provides a curated list of resources organized by key decision points faculty may face in their teaching.