Speaker 1 Okay, so I. It's seemingly everywhere these days, but, knowing when to actually trust it, that feels like the tricky part. Speaker 2 It really is. Especially when we talk about these decision support systems. Yeah. You know, the ones helping out in finance or even health care. Speaker 1 Exactly. So for this deep dive, we're really focusing on that connection, that link between, usability and trust in AI. Speaker 2 How easy and intuitive something is to use and how that builds confidence. Speaker 1 Right. We've looked at some summaries, heuristics, behavioral insights, that kind of thing. Plus there's this interesting comparison of how trust in AI accuracy. Well it really changes depending on the industry. Speaker 2 Yeah quite dramatically in some cases. Speaker 1 So our goal today really is to help everyone listening understand what actually makes an AI system feel trustworthy. Speaker 2 And a big spoiler here. Good design. It's a huge piece of that puzzle. Speaker 1 Okay. So let's dive in. One thing that really jumped out at me was this core idea. Usability drives trust. There was even this quote. Usability is not just about ease of use. It is the gateway to trust in high stakes environments. That sounds pretty significant. Speaker 2 It absolutely is. Speaker 1 But what does that really mean? Why is usability so fundamental, especially when the stakes are high? Speaker 2 Well, fundamentally it boils down to transparency or maybe perceived transparency. Okay. When an AI system makes it clear or clear enough what it's doing and, maybe why it's doing it. Users can start building an accurate mental model. They get a sense of how it works. Right? Speaker 1 Like forming a picture in their head. Speaker 2 Exactly. And understanding, even at a basic level, is really fundamental to trust. Think about, say, a medical AI suggesting a diagnosis. Yeah. If the doctor using it can see the key data points the AI used or understand the basic logic. They're far more likely to trust that suggestion than if it just spits out an answer. You know, like a black box. Speaker 1 That totally makes sense. But I wonder. Couldn't showing too much transparency actually be overwhelming? Speaker 2 Oh for sure, that's a real risk. Speaker 1 Especially in something complex like medicine. You can't expect a doctor to suddenly become a machine learning expert, right? Speaker 2 No. Absolutely not. And that's precisely where good, thoughtful design comes into play. It's not about dumping everything on the user. Speaker 1 So what's the solution then? Speaker 2 We can use techniques like, progressive disclosure. It sounds fancy, but it just means revealing information in layers. Speaker 1 Okay. Layers. How does that work? Speaker 2 So maybe initially the doctor just sees the top level diagnosis or recommendation. Clear and simple. Got it. But then if they want to know more, they can click or interact somehow to, you know, peel back a layer, see more details about the data or the AI's confidence level or the main factors it considered. Speaker 1 So it's about giving users the right information at the right time without flooding them initially. Speaker 2 Precisely. It's a balancing act. Enough info to build trust, but not so much that it causes confusion or overload. Speaker 1 That actually reminds me of something from the Nielsen Army Group findings, their emphasis on the visibility of system status. Speaker 2 A classic usability principle. Speaker 1 Yeah. They talk about how frustrating it is when you don't know what's happening. Like those awful loading screens with no progress bar. You just sit there wondering exactly. Speaker 2 Is it working? Is it stuck? That uncertainty completely erodes trust if the system isn't telling you what it's doing. How can you rely on it? Speaker 1 It really connects back to the psychology of it all, doesn't it? Speaker 2 It really does because we have these known biases, right? On one hand there's automation bias. Speaker 1 Where we trust the machine too much. Speaker 2 Yeah, we over rely on it sometimes, even when our own intuition or other evidence suggests it might be wrong. Okay. But then there's the flip side algorithm aversion, which is where maybe the system makes just one mistake, even a small one, and suddenly we completely reject it. We won't trust it at all, even if it's generally very accurate. Speaker 1 So we swing between blind faith and total rejection. Speaker 2 Pretty much. And good usability design, especially with transparency and feedback, can help us find that healthier middle ground. It helps us calibrate our trust appropriately. Speaker 1 Speaking of calibration. Yeah, that cross industry comparison we mentioned. It showed that this appropriate trust level varies wildly. Speaker 2 Oh, absolutely. Speaker 1 Context is king, like you mentioned, finance and health care earlier. What was the really striking difference there? Speaker 2 Well, in finance say with an AI tool suggesting investments, people might tolerate a small margin of error. Yeah. If the system is transparent about its process and maybe its confidence levels, they can weigh the risks. Right? But in health care, the stakes are just so much higher. Life and death potentially. Yeah. So even an AI that's statistically very accurate, maybe more accurate than the average human doctor on paper, it won't necessarily be trusted. Speaker 1 Why not if it's more accurate? Speaker 2 Because trust there isn't just about raw accuracy. It needs rigorous clinical validation. Doctors need proof. It works reliably in real world patient scenarios. And crucially, it has to fit seamlessly into their existing workflow. Speaker 1 The workflow aspect. Speaker 2 Yeah. If it's clunkier, disruptive doctors just won't use it, no matter how accurate it claims to be. The system accuracy trust comparison source really hammered this home. Trust is deeply tied to context and integration. Speaker 1 That's fascinating. So usability isn't just about the screen design. It's about how the whole system fits into the real world. Speaker 2 Exactly. Speaker 1 So for the designers listening, what can they actually do? How do we build systems that genuinely earn trust across these different areas? Just making buttons bigger? Speaker 2 If only it were that simple. No. Ease of use is definitely part of it, but it's far from the whole story. Speaker 1 So what else? Speaker 2 We need to prioritize that transparency. We talked about giving users real insight into the AIS reasoning, even if simplified. Speaker 1 Okay. Transparency first. Speaker 2 Then we need to actively support user agency. Agency meaning? Meaning giving the user a sense of control, the ability to perhaps override the AI suggestion if they disagree strongly, or maybe adjust certain parameters the AI uses. Speaker 1 Like tweaking the settings. Speaker 2 Yeah. Think back to that financial AI. If a user can say, okay, I like your suggestions, but I want to avoid investments in fossil fuels or dial down the risk level a bit, that empowers them. Speaker 1 And that builds trust because they're not just passively accepting whatever the black box tells them. Speaker 2 Exactly. It becomes more a collaboration. Speaker 1 That idea of user control of agency seems especially important because, well, let's be honest, I doesn't always get it right. Speaker 2 Definitely not. Speaker 1 The Nielsen Norman Group pointed out this big problem many AI tools kind of over promise and under deliver. Speaker 2 The hype cycle. Yeah. Speaker 1 And when that happens users get disappointed, disillusioned. And that obviously kills trust. Speaker 2 It does. So giving users that control that agency can act as a buffer. Even if the AI isn't perfect. The user feels less like they're just stuck with a faulty tool. They have some recourse. And another crucial point is designing for trust calibration over time. It's not just about establishing trust. Initially. Speaker 1 You mean how trust evolves as you use the system. Speaker 2 Precise. It's important to maybe strategically expose users to the full range of the system's capabilities, and that includes its limitations. And yes, even its failures. Speaker 1 Expose them to failures. That sounds risky. Speaker 2 It sounds counterintuitive, I know. But think about it like this. Would you fully trust an airline pilot who boasts they've never encountered turbulence or had to handle a tricky crosswind? Speaker 1 Probably not. You'd want someone with experience handling challenges. Speaker 2 Exactly. It's similar with AI, seeing how a system performs in edge cases, or even seeing it make a mistake and perhaps recover or flag its own uncertainty that builds a more realistic, robust kind of confidence. It helps users understand when and how much to trust it. Speaker 1 So it's about building realistic expectations, not just blind faith. Speaker 2 That's the key. Realistic expectations and informed trust. Speaker 1 Okay, this has been really insightful. So the big takeaway seems clear. Usability is absolutely foundational for building trust in AI. Definitely. But it's a much richer concept than just, you know, easy to use. It's deeply tied into transparency, giving users control, managing expectations. Speaker 2 And making sure it all fits within the specific context where the AI is being. Speaker 1 Used. Right. It's about the whole experience. Now that leaves me with a final thought. Something maybe for our listeners to ponder. We talked a lot about transparency. Speaker 2 Yeah. Speaker 1 But what happens with this phenomenon. The Nielsen group mentioned these AI hallucinations. Speaker 2 Yes. When the AI basically makes things up but sounds totally convincing while doing it. Speaker 1 Exactly. It sounds incredibly confident, but it's just wrong. So the question is, how can we possibly design systems to help users detect those hallucinations? How do we build in safeguards or cues. So people aren't easily misled when the AI sounds so sure of itself, but it's actually off the rails. Speaker 2 That is a really challenging design problem and a critical one as these systems become more conversational. Speaker 1 Definitely something to think about.