Thrilled to be part of the new special edition of Information, Machine Learning w/ #Python, guested edited by @rasbt (mdpi.com/journal/information…). Our paper (mdpi.com/2078-2489/11/3/137) presents interpretable #DeepLearning for structured data post-hoc #xai discrimination testing.

Two recent papers explore a common question: What occurs when we train new generative models using data that includes content generated by earlier models? • The Curse of Recursion: Training on Generated Data Makes Models Forget Using model-generated content in training causes irreversible defects. The tails of the original content distribution disappear. Within a few generations, text becomes garbage, and authors call this effect model collapse. • Self-Consuming Generative Models Go MAD[2] The lack of sufficient fresh real data in each generation of a self-consuming loop leads to a progressive decrease in the quality and diversity of future generative models. We refer to this condition as Model Autophagy Disorder (MAD). [1] arxiv.org/pdf/2305.17493v2.p… [2] arxiv.org/pdf/2307.01850.pdf

Just received the hard copy of my book. It's a surreal feeling to finally hold it in my hands. Thanks to everyone who made it possible.🙏

"Using the NIST AI Risk Management Framework" @ElhamTabassi Reva Schwartz @baxterkb @sinafazelpour @jpatrickhall ...how to use the AI RMF to enhance organizational AI risk management posture, governance mechanisms, operationalize AI system trustworthiness, and more...

#TheDataExchangePod: @jpatrickhall @bnh_ai @gwbusiness and Agus Sudjianto @WellsFargo dive into the world of #ResponsibleAI and into practical techniques for developing secure, unbiased, explainable, and reliable #MachineLearning #AI models. thedataexchange.media/machin…

#TheDataExchangePod: @jpatrickhall @bnh_ai @gwbusiness and Agus Sudjianto @WellsFargo dive into the world of #ResponsibleAI and into practical techniques for developing secure, unbiased, explainable, and reliable #MachineLearning #AI models. thedataexchange.media/machin…

#TheDataExchangePod: @jpatrickhall @bnh_ai @gwbusiness and Agus Sudjianto @WellsFargo dive into the world of #ResponsibleAI and into practical techniques for developing secure, unbiased, explainable, and reliable #MachineLearning and #AI models. thedataexchange.media/machin…

Thankful to my co-authors, @jpatrickhall & James Curtis. Also grateful to Agus Sudjianto for writing the foreword for the book.

Remember when you went to Microsoft for stodgy but basically functional software and the bookstore for speculative fiction? arXiv may have been useful in physics and math (and other parts of CS) but it's a cesspool in "AI"—a reservoir for hype infections arxiv.org/abs/2303.12712

📣 Excited to announce that our book on #MachineLearning for High-risk applications has finally reached the finish line 🏁 and it is going into production! It was a mammoth task, but the final draft is now complete. Pre-release on @OReillyMedia buff.ly/3E0xOa8

Also big shoutout to my co-authors, including @jpatrickhall, who made this possible.

.@nist has just released the #AI Risk Management Framework (AI RMF 1.0) to promote #responsible development and use of AI systems - includes companion playbook, explainer video, roadmap, and crosswalk w/ industry use cases #NIST nvlpubs.nist.gov/nistpubs/ai… @navrinasingh @jpatrickhall

We'll be joining ⬇️ Agus Sudjianto - EVP Head of Corporate Model Risk @WellsFargo @JPatrickHall - Principal Scientist BNH.AI & Visiting Faculty at @gwbusiness @SriKrishnamurthy - President of @quantUniversity Rafic Fahs - Chief Model Risk Officer @FifthThird

Join us as @jpatrickhall surveys how PiML can help you streamline model development and address challenges in transparency, fairness, and other areas as you harness the power of ML for your work. 🗓️ Jan 20 🕚 11 AM EST Register: hopin.com/events/quality-ai-… #TechShares

Here are all the ways to get around ChatGPT's safeguards: [1/n]

What We Learned Auditing Sophisticated #AI for #Bias oreilly.com/radar/what-we-le… via @oreillymedia @jpatrickhall @bnh_ai