About this episode
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. Join the discussion Changelog++ members save 2 minutes on this episode because they made the ads disappear. Join today! Sponsors: Linode – Our cloud of choice and the home of Changelog.com. Deploy a fast, efficient, native SSD cloud server for only $5/month. Get 4 months free using the code changelog2019 OR changelog2020. To learn more and get started head to linode.com/changelog . Pace.dev – Minimalist web based management tool for your teams. Async by default communication and simplistic task management gives you everything you need to build your next thing. Brought to you by Go Time panelist Mat Ryer. Try it out today! Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com . 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!