Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
HAI Weekly Seminar with Art Owen | Stanford HAI
Skip to content
  • About

    • About
    • People
    • Get Involved with HAI
    • Support HAI
    • Subscribe to Email
  • Research

    • Research
    • Research Programs
    • Grants
    • Marlowe (opens in new tab)
    • Student Affinity Groups
    • Centers & Labs
    • Research Publications
    • Research Partners
  • Education

    • Education
    • Executive and Professional Education
    • Government and Policymakers
    • K-12
    • Stanford Students
  • Policy

    • Policy
    • Policy Publications
    • Policymaker Education
    • Student Opportunities
  • AI Index

    • AI Index
    • AI Index Report
    • Global Vibrancy Tool
    • People
  • News
  • Events
  • Industry
  • Centers & Labs
Navigate
  • About
  • Events
  • AI Glossary
  • Careers
  • Search
Participate
  • Get Involved
  • Support HAI
  • Contact Us

Stay Up To Date

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.

Sign Up For Latest News

Your browser does not support the video tag.
eventSeminar

HAI Weekly Seminar with Art Owen

Status
Past
Date
Wednesday, September 29, 2021 10:00 AM - 11:00 AM PST/PDT
Location
Virtual
Topics
Design, Human-Computer Interaction
Overview
Watch Event Recording

Variable Importance, Cohort Shapley Value, and Redlining

Overview
Watch Event Recording
Share
Link copied to clipboard!
Event Contact
Kaci Peel
kpeel@stanford.edu

Related Events

Marlowe | AI + Data for Science with Curtis Langlotz
SeminarOct 14, 2026
October
14
2026

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

Event

Marlowe | AI + Data for Science with Curtis Langlotz

Oct 14, 2026

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

Serena Wang: From Metric Selection to Metric Discovery in AI Evaluation
SeminarOct 16, 20261:00 PM - 2:15 PM
October
16
2026
Seminar
Your browser does not support the video tag.

Serena Wang: From Metric Selection to Metric Discovery in AI Evaluation

Oct 16, 20261:00 PM - 2:15 PM
Carina Hong | AI That Can Prove It: From Mathematical Discovery to Verified Software
Oct 19, 202612:00 PM - 1:00 PM
October
19
2026

A eureka moment is a two-step process: discovering something new and surprising, and establishing that it is correct. Drawing on her work at Axiom, Carina will discuss the transition from solving mathematical benchmarks to contributing to research, and from proving individual theorems to formalizing mathematical theories in Lean.

Event

Carina Hong | AI That Can Prove It: From Mathematical Discovery to Verified Software

Oct 19, 202612:00 PM - 1:00 PM

A eureka moment is a two-step process: discovering something new and surprising, and establishing that it is correct. Drawing on her work at Axiom, Carina will discuss the transition from solving mathematical benchmarks to contributing to research, and from proving individual theorems to formalizing mathematical theories in Lean.

Review Art's Slideshow Here

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.

Art Owen
Professor of Statistics, Stanford University