Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

People Are Not Coins - Morally Distinct Types of Predictions Necessitate Different Fairness Constraints

Association for Computing Machinery (ACM) via YouTube

Overview

Explore a thought-provoking 15-minute conference talk that challenges conventional notions of fairness in predictive systems. Delve into the argument presented by Eleonora Viganó, Corinna Hertweck, Christoph Heitz, and Michele Loi that different types of predictions require distinct fairness constraints. Examine the analogy of people not being coins to understand why a one-size-fits-all approach to fairness in AI and machine learning may be inadequate. Gain insights into the moral implications of various prediction types and how they necessitate tailored approaches to ensure ethical and equitable outcomes in algorithmic decision-making processes.

Syllabus

People are not coins: Morally distinct types of predictions necessitate different fairness...

Taught by

ACM FAccT Conference

Reviews

Start your review of People Are Not Coins - Morally Distinct Types of Predictions Necessitate Different Fairness Constraints

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.