YoVDO

Algorithmic Fairness, Loss Minimization and Outcome Indistinguishability

Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube

Tags

Algorithmic Fairness Courses Machine Learning Courses Fairness in AI Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a 51-minute lecture on algorithmic fairness, loss minimization, and outcome indistinguishability presented by Omer Reingold from Stanford University at IPAM's EnCORE Workshop. Delve into the limitations of traditional loss minimization in machine learning when addressing algorithmic fairness concerns. Examine multi-group fairness notions like multicalibration and their connection to computational indistinguishability. Discover how outcome indistinguishability offers an alternative paradigm for training predictors that can be applied to various loss functions, capacity constraints, fairness requirements, and instance distributions. Gain insights into this growing field with applications beyond fairness in machine learning.

Syllabus

Omer Reingold - Algorithmic Fairness, Loss Minimization and Outcome Indistinguishability


Taught by

Institute for Pure & Applied Mathematics (IPAM)

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