Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219
Published:Jan 10, 2019 18:22
•1 min read
•Practical AI
Analysis
This article discusses a podcast episode featuring Solon Barocas, an Assistant Professor at Cornell University. The conversation focuses on the legal and policy implications of machine learning model interpretability. The discussion explores the disconnect between law, policy, and machine learning, and the need to bridge this gap. The episode also touches upon formalizing ethical frameworks for machine learning and Barocas's paper, "The Intuitive Appeal of Explainable Machines." The core theme revolves around the challenges and opportunities presented by the increasing use of AI in various sectors and the necessity of establishing clear guidelines and regulations.
Key Takeaways
- •The podcast episode discusses the legal and policy implications of machine learning model interpretability.
- •The conversation explores the gap between law, policy, and machine learning.
- •The episode touches upon formalizing ethical frameworks for machine learning.
Reference
“In our conversation, we explore the gap between law, policy, and ML, and how to build the bridge between them, including formalizing ethical frameworks for machine learning.”