In an age when AI can get done what humans historically could across a growing list of tasks, a fundamental question lingers: what does it take for people to fully feel confident in these systems’ ability to make fully automated decisions?
This question is particularly prescient in businesses working in high-stakes environments with strict legal and industry regulations, as a single error can jeopardize revenue, put partnerships at risk, or damage a company’s reputation.
Min Chun Fu, a 22-year-old founding engineer at Comp AI, a platform that automates evidence gathering, policy checks, and compliance audits through transparent, observable AI workflows, is currently working to incorporate that confidence into the growing AI industry. He argues the next stage of AI won’t be defined by greater computational force but by a discipline long treated as secondary: transparent design.
Addressing the AI Trust Gap
AI can deal with technical tasks that typically require entire teams, from reviewing code for compliance risks to verifying whether company accounts enforce essential protections like two-factor authentication. It can move through environments such as Google admin dashboards or GitHub settings with speed and precision, completing checks that previously took days.
But despite this capability, a central problem persists: people still hesitate to trust what they cannot see.
That reluctance stems from the “black box” nature of most systems. When users can’t follow how an output was generated, it can generate doubts and concerns regarding its internal logic. Further adding to this is that many models still struggle to retain and properly contextualize all the information sent their way, which opens the possibility to outputs that might look correct but contain made-up information — what are commonly known as “hallucinations.”
This has created a valuable and potentially powerful industry, but also one rampant with skepticism, as even correct results can risk becoming second-guessed.
Min Chun Fu believes that for this technology’s credibility to be restored, every step it takes to come up with an output needs to be visible and understandable. He argues that people should feel confident that an automated check reflects what actually happened rather than what a model inferred. “AI can already do the work,” he says, “but if people don’t believe it, it doesn’t matter.”
Min Chun Fu’s Belief: Transparency as a Main Feature
Through his experiences at Comp AI, Fu believes that making a product in this field trustworthy consists of making its main technical functioning clear and transparent. He argues that systems earn confidence only when users can follow and understand how decisions are made, as opposed to simply getting the final output.
In practice, that means showing steps instead of hiding them. Companies can do this by providing features like audit trails and live sandbox views that create a live record for each prompt, turning a potentially opaque flow into something traceable.
Setting up features that show, in no uncertain terms, what the system accessed, what it checked, and what it ignored, gives people a grounded sense of control. As Fu puts it, “You can’t just tell users to trust AI. You have to show them why they can.”
This philosophy shapes the work he leads at Comp AI. Under his direction, each of the platform’s audits is designed to display its own evidence chain, laying actions like script execution and data verification in a way that aims to imitate how a human would act on this task. The company’s goal is to set an environment where automation feels less like a hidden mechanism and more like a partner whose work can be verified at every turn.
The Importance of Explainable Design
Another principle Fu emphasizes is how the role of design can better help people relate to automated systems. He summarizes it by saying, “Everything should be crafted with care because details are how people decide whether they’re going to trust you.”
Design becomes the way that the logic behind a product’s intention is translated for regular use. Building a clean visual hierarchy can help users follow what the system is doing and can help them organize themselves mentally when first interacting with the product. Clear copy removes ambiguity, guiding interpretation rather than forcing guesswork. Color cues, confirmations, and progress indicators turn abstract machine processes into steps that feel easy to follow.
When design respects the user’s perspective, according to Fu, it humanizes the underlying technology and makes it easier for day-to-day use.
At Comp AI, Fu builds navigation workflows structured to surface the right information at the right moment, eliminating friction during high-stakes compliance work. He treats design as a key aspect of the final infrastructure in an attempt to help users move through the product with clarity and confidence.
How Trust Starts in Early Development
In Fu’s view, trust begins with the culture that produces the technology itself. He believes teams must set clear internal standards for how a system should communicate, behave, and reveal its reasoning — and these expectations must be built into the core development process.
As a co-founding engineer at Comp AI, he puts this into practice by constantly interacting with multiple teams across the design, product, and marketing stages to make sure there’s a shared understanding of product decisions and internal norms. “As a founding engineer, you have to ask: will this decision make users more confident or more confused?” he says.
He believes working with that mindset from the start can strengthen not only the product but the organization behind it. When teams prioritize openness, they can build a system that inspires trust more naturally and avoids the confusion that can surround automated systems.
A Call for a More Accessible Future of AI
Fu sees the future of AI shaped less by raw computational strength and more by how appealing the end system is to users. He believes that as automated tools become central to decision-making, people will expect clearer insight into how those tools operate, where their boundaries lie, and how their conclusions were formed.
In his view, future AI entrepreneurs and engineers will need to lean on treating transparency as a core product value from the start, one that offers products that feel understandable, visible, and grounded in how regular users act — and Min Chun Fu sees his work as part of that shift. In his own words, “The real AI revolution isn’t about intelligence. It’s about trust.”
