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Alternative AI FuturesSee Workshop Series: Alternative AI FuturesAlternative AI Futures is a hands-on workshop series that invites students to directly experiment with, critique, and modify contemporary AI systems in order to reimage how different design choices, cultural values, and shared goals could produce radically different forms of AI.
Each workshop will begin with a simple but provocative prompt: What if AI were built differently? More specifically, what if factors like transparency, accountability, and social responsibility were the primary values guiding AI system design? What would those systems look like? How would they behave? And how might they address commonly held concerns about the structural limitations and/or social consequences of AI? These “what if” questions will foreground the assumptions embedded in current AI systems and open space for imagining alternative futures for how AI might be built and used differently. -
Training For ToxicitySee Workshop Series: Training For ToxicityTraining for Toxicity is a three-part workshop series in which students fine-tune a pre-trained language model into a text classifier from scratch, interrogating the assumptions embedded at each stage of the pipeline.
Working with real posts from far-right Discord channels, students develop their own taxonomies for toxic masculinity, independently label a shared set of posts, and confront the disagreements that emerge. In the final workshop, students upload their labeled data into a custom interface, train a model, and examine its behavior by testing new inputs, analyzing which training examples the model considers most similar, and toggling categories on and off to observe how training data shapes classification. Across the series, students confront what each stage of the pipeline discards: the partial agreements behind categories, the context and nuance flattened by labels, and the reasoning lost once human judgment is compressed into training data.