When AI goes wrong

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

Something missing or broken? PRs welcome!

Creators & Guests

Host

Chris Benson

Host

Daniel Whitenack

Guest

Andrew Burt

Guest

Patrick Hall

What is Practical AI?

Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!