Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University.

Solon and I caught up to discuss his work on model interpretability and the legal and policy implications of the use of machine learning models. 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. We also look at his paper ”The Intuitive Appeal of Explainable Machines.”

Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.

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The complete show notes for this episode can be found at twimlai.com/go/682.

Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682

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The complete show notes for this episode can be found at twimlai.com/go/681.

GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681

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The complete show notes for this episode can be found at twimlai.com/go/680.

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The complete show notes for this episode can be found at twimlai.com/go/679.

Localizing and Editing Knowledge in LLMs with Peter Hase - #679

Today we're joined by Jonas Geiping, a research group leader at the ELLIS Institute, to explore his paper: "Coercing LLMs to Do and Reveal (Almost) Anything". Jonas explains how neural networks can be exploited, highlighting the risk of deploying LLM agents that interact with the real world. We discuss the role of open models in enabling security research, the challenges of optimizing over certain constraints, and the ongoing difficulties in achieving robustness in neural networks. Finally, we delve into the future of AI security, and the need for a better approach to mitigate the risks posed by optimized adversarial attacks.

The complete show notes for this episode can be found at twimlai.com/go/678.

Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219

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