eventSeminar
HAI Weekly Seminar with Art Owen
Variable Importance, Cohort Shapley Value, and Redlining
Hari Subramonyam | Learning by Creating: A Human-Centered Vision for AI in Education
SeminarMar 11, 202612:00 PM - 1:15 PM
March
11
2026
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Variable Importance, Cohort Shapley Value, and Redlining
The African Olympiad Academy is a world-class high school dedicated to training Africa’s most promising students in mathematics, science, and artificial intelligence through olympiad-based pedagogy.
The African Olympiad Academy is a world-class high school dedicated to training Africa’s most promising students in mathematics, science, and artificial intelligence through olympiad-based pedagogy.
Despite the rapid adoption of LLM chatbots, little is known about how they are used. We approach this question theoretically and empirically, modeling a user who chooses whether to complete a task herself, ask the chatbot for information that reduces decision noise, or delegate execution to the chatbot...
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Despite the rapid adoption of LLM chatbots, little is known about how they are used. We approach this question theoretically and empirically, modeling a user who chooses whether to complete a task herself, ask the chatbot for information that reduces decision noise, or delegate execution to the chatbot...
In order to explain what a black box algorithm does, Owen says, we can start by studying which variables are important for its decisions. Variable importance is studied by making hypothetical changes to predictor variables. Changing parameters one at a time can produce input combinations that are outliers or very unlikely. They can be physically impossible, or even logically impossible. It is problematic to base an explanation on outputs corresponding to impossible inputs. Owen introduces the cohort Shapley (CS) measure to avoid this problem, based on Shapley value from cooperative game theory.
There are many tradeoffs in picking a variable importance measure, so CS is not the unique reasonable choice. One interesting property of CS is that it can detect 'redlining', meaning the impact of a protected variable on an algorithm's output when that algorithm was trained without the protected variable.
