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