
AI, reliability and trust
Philosopher's Zone
Trust Issues in Black Box AI
This chapter explores the skepticism surrounding black box AI systems, highlighting the need for transparency and explainability in AI to build public trust. It discusses a 2024 federal policy advocating for clearer AI methodologies to foster acceptance in both government and commercial applications.
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Speaker 2
Let me make sure I understand. What's the way that
Speaker 1
you can test out these different hypotheses? Do you have an animal model of Huntington's? Yeah, so not speaking specifically about Huntington's, but just generally in terms of different diseases. Some diseases, you'll have an animal model that's really good. And that means you can actually try it out like in mice or maybe even monkeys and like really get a lot of confidence. For others, honestly, you don't have anything. Maybe there's no animal model. So like you have to try it out in cell lines and then kind of make your best guess about what's going to work well. So it's very varied, I'd say, across different diseases and across different tissues. But you want to have as many shots on goal, I think, as possible. Because then when you go to humans, maybe you'll find something surprising. Like you only had cell line work and then it doesn't work in humans. And if you only have one technique, you're kind of out of luck. But if you have like, you know, a backup or a backup to the backup, that means you can actually go in and, you know, do something for patients after that.
Speaker 2
Got it.
AI is making all kinds of important decisions for us these days, but how far can we trust it? Or rather, what kind of trust is appropriate to bring to AI? The inner workings of "black box" AI are inscrutable even to its creators, so if transparency and explainability are essential to the development of trust, then we could be in trouble. It all depends on how we think about trust.