Exploring the NIST AI Risk Management Framework (RMF) with Patrick Hall

The AI Fundamentalists

Join us as we chat with Patrick Hall, Principal Scientist at Hallresearch.ai and Assistant Professor at George Washington University. He shares his insights on the current state of AI, its limitations, and the potential risks associated with it. The conversation also touched on the importance of responsible AI, the role of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF) in adoption, and the implications of using generative AI in decision-making.
Show notes
Governance, model explainability, and high-risk applications 00:00:03

• Intro to Patrick
• His latest book: Machine Learning for High-Risk Applications: Approaches to Responsible AI (2023)

The benefits of NIST AI Risk Management Framework 00:04:01

• Does not have a profit motive, which avoids the potential for conflicts of interest when providing guidance on responsible AI.
• Solicits, adjudicates, and incorporates feedback from the public and other stakeholders.
• NIST is not law, however it's recommendations set companies up for outcome-based reviews by regulators.

Accountability challenges in "blame-free" cultures 00:10:24

• Cites these cultures have the hardest time with the framework's recommendations
• Practices like documentation and fair model reviews need accountability and objectivity
• If everyone's responsible, no one's responsible.

The value of explainable models vs black-box models 00:15:00

• Concerns about replacing explainable models with LLMs for LLM's sake
• Why generative AI is bad for decision-making

AI and its impact on students 00:21:49

• Students are more indicative of where the hype and market is today
• Teaching them how to work through the best model for the best job despite the hype

AI incidents and contextual failures 00:26:17

• AI Incident Database
• AI, as it currently stands, is a memorizing and calculating technology. It lacks the ability to incorporate real-world context.
• McDonald's AI Drive-Thru debacle is a warning to us all

Generative AI and homogenization problems 00:34:30
Recommended resources from Patrick:

• Ed Zitron “Better Offline”
• NIST ARIA
• AI Safety Is a Narrative Problem

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章节

1. Exploring the NIST AI Risk Management Framework (RMF) with Patrick Hall (00:00:00)

2. Governance, model explainability, and high-risk applications (00:00:03)

3. The benefits of NIST AI RMF (00:04:01)

4. Accountability challenges in "blame-free" cultures (00:10:24)

5. The value of explainable models vs black box models (00:15:00)

6. AI and its impact on students (00:21:49)

7. AI incidents and contextual failures (00:26:17)

8. Generative AI and homogenization concerns (00:34:30)