September 14, 2020

When AI goes wrong (Practical AI #105)

58 minutes

So, you trained a great AI model and deployed it in your app? It’s smooth sailing from there right? Well, not in most people’s experience. Sometimes things goes wrong, and you need to know how to respond to a real life AI incident. In this episode, Andrew and Patrick from BNH.ai join us to discuss an AI incident response plan along with some general discussion of debugging models, discrimination, privacy, and security.

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Featuring:

Andrew Burt – Website, X
Patrick Hall – GitHub, X
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X

Show Notes:

AI Incident Response Checklist and other BNH.ai resources

“New Law Firm Tackles AI Liability” (article about BNH.ai)

In the realm of paper tigers – exploring the failings of AI ethics guidelines

Debugging Machine Learning Models workshop

Why you should care about debugging machine learning models

Strategies for model debugging

FTC: Using Artificial Intelligence and Algorithms

SR 11-7: Guidance on Model Risk Management

Apple Goldman case

California Consumer Privacy Act (CCPA)

Previous episode: Data management, regulation, the future of AI

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