YoVDO

Toward a Theory of Race for Fairness in Machine Learning

Offered By: Association for Computing Machinery (ACM) via YouTube

Tags

ACM FAccT Conference Courses Machine Learning Courses Algorithmic Decision-Making Courses

Course Description

Overview

Explore a thought-provoking tutorial from the FAT* 2019 conference that delves into the critical intersection of race theory and fairness in machine learning. Examine how computer scientists approach minimizing disparate impacts across racial categories in algorithmic systems, while questioning the underlying concept of race itself. Gain insights into critical race theory and social scientific discourses, and learn how these concepts can be translated for machine learning practitioners. Participate in small-group activities that demonstrate the relevance of these theories to fairness problems in AI. Note that due to technical issues, video images are unavailable for the first 2:34 minutes, but audio is fully accessible throughout the 43-minute presentation. Access the accompanying slides for a comprehensive understanding of this important topic in ethical AI development.

Syllabus

FAT* 2019 Translation Tutorial: Toward a Theory of Race for Fairness in Machine Learning


Taught by

ACM FAccT Conference

Related Courses

Introduction to Artificial Intelligence
Stanford University via Udacity
Natural Language Processing
Columbia University via Coursera
Probabilistic Graphical Models 1: Representation
Stanford University via Coursera
Computer Vision: The Fundamentals
University of California, Berkeley via Coursera
Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent