Explore a framework for accountable algorithmic systems in this 19-minute conference talk from the FAccT 2021 virtual event. Delve into the concept of reviewable automated decision-making as presented by researchers J. Cobbe, M. Lee, and J. Singh. Gain insights into the challenges and potential solutions for creating more transparent and accountable AI systems. Examine the intersection of technology, ethics, and policy as the speakers discuss their research findings and propose strategies for improving algorithmic accountability. Learn about the importance of human oversight in automated decision-making processes and discover how this framework can be applied to various sectors utilizing AI-driven systems.
Reviewable Automated Decision-Making - A Framework for Accountable Algorithmic Systems
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems
Taught by
ACM FAccT Conference
Related Courses
-
The Algorithmic Leviathan - Arbitrariness, Unfairness, and Opportunity in Algorithmic Decision Making
-
Fairness, Equality, and Power in Algorithmic Decision-Making
-
Algorithmic Decision Making - Exploring Practical Approaches to Liability, Fairness, and Explainability
-
Designing Accountable Systems
-
I Agree with the Decision, but They Didn't Deserve This - Future Developers' Perception of Fairness
-
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
