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Zoom registration: https://uci.zoom.us/meeting/register/iehD6KfJQsy6jM_9DVY0Hg#/registration 

This presentation explores a collaboration with and participant observation of computer scientists working in artificial intelligence (AI) algorithm design. Our cross-disciplinary collaboration was built upon a fundamental methodological principle shared between machine learning and ethnographic research: that is, the principle of iterative refining and validating a learning algorithm or developing theory on new data input. Our collaboration explored the methodological articulation between these two supposedly distanced fields and experimentally applied the result in our NSF funded project, “Human-in-the Loop Fairness Optimization in Machine Learning.” We employed multiple strategies to consider the contextual factors and human agency in algorithm design and product use. This talk will outline these strategies as a mode for how ethnographic habits of knowing find forms and functions in AI algorithm design.

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