Fair Classification with Group-Dependent Label Noise
Offered By: Association for Computing Machinery (ACM) via YouTube
Course Description
Overview
Explore a 21-minute conference talk from the FAccT 2021 virtual event that delves into the challenges of fair classification in the presence of group-dependent label noise. Presented by J. Wang, Y. Liu, and C. Levy as part of the Research Track, this talk examines the impact of biased data labeling on machine learning models and proposes solutions to mitigate unfairness in classification tasks. Learn about the researchers' approach to addressing this critical issue in AI ethics and fairness, and gain insights into potential strategies for improving the accuracy and equity of machine learning systems when dealing with noisy, group-dependent labels.
Syllabus
Fair Classification with Group-Dependent Label Noise
Taught by
ACM FAccT Conference
Related Courses
Translation Tutorial - Thinking Through and Writing About Research Ethics Beyond "Broader Impact"Association for Computing Machinery (ACM) via YouTube Translation Tutorial - Data Externalities
Association for Computing Machinery (ACM) via YouTube Translation Tutorial - Causal Fairness Analysis
Association for Computing Machinery (ACM) via YouTube Implications Tutorial - Using Harms and Benefits to Ground Practical AI Fairness Assessments
Association for Computing Machinery (ACM) via YouTube Responsible AI in Industry - Lessons Learned in Practice
Association for Computing Machinery (ACM) via YouTube