Human Centered AI: Building for Trust at the Edge of Complexity

FTX collapsed in a matter of days. Not because the code failed, but because the trust did. Speed and opacity formed a toxic combination. No one could see what was happening until it was too late.

Wikipedia, on the other hand, endures. A decentralized platform that anyone can edit has somehow become one of the most trusted sources on the internet. Why? Not because it’s flawless, but because it’s transparent. You can see every change, every edit history, every talk page dispute.

Tesla’s Autopilot system? It teeters somewhere in the middle. A marvel of machine learning, yes, but prone to unpredictable edge cases and mixed messaging. Users aren’t always sure when it’s in control, or how it will behave. That’s not a technical issue. That’s a trust issue.

Trust isn’t a vibe. It isn’t something you add on at the end. It’s not a brand campaign or a reassuring tagline. It’s an emergent property of well-designed systems.

Trust isn’t a vibe. It’s an emergent property of well-designed systems

Trust Is a System Property

We tend to talk about trust like it’s an emotion. In consumer products, it's often reduced to brand loyalty. In tech, it’s framed as a risk calculation: Is the system secure? Is it private? Is it fair?

But when you zoom out, trust in any system, especially high-complexity systems like autonomous vehicles or large language models, emerges from structure.

The moment users understand how a system works, what it will likely do next, and why it behaved a certain way, trust begins to form. Not as a feeling, but as a byproduct of clarity, predictability, and interpretability. We call this the Trust Stack.

The Trust Stack: Clarity → Predictability → Interpretability

If trust is the outcome, these three are the inputs:

• Clarity - What does this system do? What is it for? What is it not for? What boundaries define its behavior?
• Predictability - When I use it, will I get roughly the same result under similar conditions? Can I anticipate how it will respond to my actions?
• Interpretability - If something goes wrong, or even when it goes right, can I understand why? Can I trace the decision or output back to something intelligible?

When any layer is missing, trust begins to erode. Unfortunately, most AI systems today are missing all three.

The Struggle is Real

I. Clarity

Most people still don’t understand how LLMs work. And to be fair, they were never meant to. But the dominant interface pattern, “ask me anything”, doesn’t help.

These models aren’t search engines. They’re probabilistic systems trained to generate the most likely next token given a huge corpus of text. That means they predict, not retrieve. And yet most users engage with them as if they were deterministic oracles. The result? Users walk away feeling like they’ve failed, when in reality, the system was never clear to begin with.

II. Predictability

Anyone who has used ChatGPT or Claude for more than a few hours has run into this: same input, different results. Or worse, slight changes in phrasing yielding dramatically better (or worse) outputs with no explanation.

This lack of behavioral consistency means users can’t form accurate mental models of how the system works. Without those models, they can’t build workflows, can’t trust outputs, and can’t scale usage beyond experimentation.

This unpredictability isn’t always a bug, it’s often a natural outcome of the model’s architecture. But when the system offers no cues or guidance for how to reduce that unpredictability, users are left guessing.

III. Interpretability

This is perhaps the most acute failure. AI tools rarely tell you why they gave you a particular result. There are no breadcrumbs. No explanation of which part of your prompt mattered. No insight into how prior messages shaped the response. No feedback loops that let users inspect or adjust the system’s “reasoning.”

In high-stakes domains, legal research, medical summaries, financial planning, that interpretability gap becomes dangerous. In everyday productivity use cases, it simply becomes annoying. The end result across all three layers? Drift, fatigue, and ultimately, abandonment.

Designing at the Edge of Complexity

We need to stop pretending that simplicity equals usability. The reason so many AI tools fall short isn’t because the models are underpowered, it’s because the interfaces weren’t designed to handle this level of complexity. A single prompt box and a blinking cursor is not an interface. It’s an invitation to frustration.

Designing for trust in this context doesn’t mean hiding the complexity. It means revealing the right parts of it at the right time.

• Instead of infinite possibility, provide scaffolding.
• Instead of black-box behavior, expose the system’s logic through context windows or prompt traces.
• Instead of open-ended improvisation, offer guided paths tailored to the user’s role or goal.

Trustable systems are not necessarily simple systems. They are navigable ones. Systems where the user knows where they are, what’s happening, and what might happen next.

From Feeling to Framework

We don’t need to convince users to trust AI. We need to give them the conditions in which trust can emerge.

That means:

• Engineering clarity into interfaces
• Designing for predictability through structured interactions
• Making systems interpretable enough for users to learn and adapt over time

The real unlock isn’t just better models. It’s better surrounding systems, interfaces that are opinionated, transparent, and grounded in human context. At Dapple, we believe trust isn’t a feature. It’s a function of the environment. One that you can architect, measure, and improve.

If we want AI to truly scale, not just technically, but socially, we need to stop designing tools for capability, and start designing infrastructure for trust.