Introduction
In today’s age of large-language models and generative systems, knowing how to design AI products users trust has moved from optional to mission-critical. Yet the roots of this challenge stretch all the way back to the 1960s, when ELIZA—the original “chatbot” built at MIT—astonished users by delivering a seemingly human-like conversation, despite using little more than pattern-matching and substitution. In this article, I argue that understanding ELIZA’s legacy is not just an academic exercise — it offers seven practical principles for building AI products that users trust, from transparency to control to monitoring, and that these become even more relevant as we move into the era of systems like ChatGPT.
Thesis statement: By drawing on the history of ELIZA and the psychology of human-machine interaction, product leaders can embed trust into AI systems from design through deployment, ensuring user confidence and lasting adoption.
The origin story – ELIZA as the “original ChatGPT” and what it teaches us
When we reflect on conversational AI today, many of us think first of ChatGPT and the new wave of generative intelligence. But the story begins with ELIZA, created by Joseph Weizenbaum between 1964 and 1966 at MIT. ELIZA worked not by deep learning or semantic understanding, but by simple pattern-matching and substitution: for example, “My mother hates me” would prompt the program to ask “Tell me more about your mother”.
Why is the origin story relevant? Because it surfaces one of the earliest lessons in “how to design AI products users trust”: even when the underlying logic is trivial, the user experience can trigger strong attribution of intelligence. Many users assumed ELIZA “understood” them — despite there being no real understanding.
From a product thinking perspective, this means: user trust often emerges from perceived agency, human-like interaction and design cues, not purely from the underlying logic. For product leaders working on AI features, recognising this legacy helps avoid the trap of assuming that “smart algorithm = trusted product”. Instead, one must design for how users interpret and engage with the system.
Transparency and understanding – why users trust (or don’t trust) AI systems
Trust in AI products isn’t just a technical issue—it’s behavioural and psychological. Research shows that building familiarity and transparency is essential for fostering trust and user confidence.
Consider three key elements:
Expectation-setting: As with ELIZA, when users felt the machine “understood” them, they engaged differently; when it failed to match expectations, trust dropped. In modern AI products, trust is lost far faster if the system over-promises and under-delivers.
Feedback and visibility: Users should see where the system got things right, where it might be uncertain, and ideally have some control to correct it. For example, showing a confidence score or an option to “edit this result” helps reinforce agency.
From a product-engineering-lead perspective, this suggests you need to bake in transparency into the UI/UX early: data provenance, uncertainty indicators, revision history—all help establishing trust. This is a key piece of how to design AI products users trust.
Control, reliability and error-handling – designing AI products users trust in practice
When building AI features, the user experience around error cases often determines trust more than the “headline” clever use case. According to UX research: delivering on your product promise, using data responsibly, and continuously monitoring performance are foundational.
For example:
Control and correction: Give users the ability to refine or override AI outputs. A study argues: “Users will trust your AI more if they feel in control of their relationship with it.”
Error transparency: When things go wrong, explain the limitations. For example: “This recommendation is based on limited data and has 60 % confidence.” That helps set expectations.
Data ethics & privacy: Collecting and using personal data for AI needs to be clearly communicated; misuse kills trust fast.
From the engineering side, this means instrumenting monitoring, establishing alerting on drift/control-charts, and defining a user-facing “trust fallback” path (e.g., human support, fallback UI). And from a product-thinking perspective, you need to design features that degrade gracefully and keep users in the loop.
Anthropomorphism, expectations & the “ELIZA effect” – managing psychology in AI product design
One of the enduring lessons from ELIZA is the so-called ELIZA effect: people attribute more intelligence and understanding to machines than is warranted.
Key take-aways:
Avoid over-emphasising “human-like” interface unless you truly support it: Too much anthropomorphism raises expectations and can backfire when the system fails. Designers of AI must balance human metaphor with clarity about machine nature.
User mental models matter: If a system uses language, the user expects something like human‐level conversation. But in many cases the AI is narrow. Managing that gap is critical to trust.
Emotional engagement vs. transparency: When ELIZA asked “Tell me more about your mother”, users sometimes divulged private issues. That reveals how strong the psychological pull can be—and how dangerous it is if the system is not backed by appropriate design guardrails.
For a modern product leader, this means designing UI/UX that clearly signals the AI’s scope, ensuring disclaimers and fallback human routes, and testing for the “illusion of understanding” risk. It also highlights part of how to design AI products users trust: anticipate and manage the user’s expectation of “human-level” intelligence.
Scaling trust – governance, fairness, continuous monitoring, and building long-term user trust
The final frontier of trust in AI products is not only at launch, but in scaling, updating, governing and preserving trust over time. Key principles include:
Governance and fairness: Modern research shows organisational context matters: transparency, fairness and integrity are key levers for user trust in AI systems.
Monitoring and feedback loops: As models evolve (or drift) the product must keep users in the loop and maintain reliability. Without this, even users who used the system initially may abandon it.
Brand, ethics and trust alignment: The product brand (your AI product) must align with user values. For example: if your AI claims “autonomous decisions”, users will want to know how decisions are audited.
User education & onboarding: Don’t assume the user knows what an AI is doing. Built-in education, micro-UX explainers and contextual help build trust.
Evolve gracefully: When you release a new model or algorithm update, manage rollout carefully, communicate changes, and allow users to opt-out or revert (where appropriate).
In short: scaling trust is not a one-time checklist — it is embedded in your product lifecycle. This is an essential part of how to design AI products users trust for the long term.
Conclusion
In this journey from ELIZA, the first “chatbot” developed in the 1960s at MIT, to the present-day era of generative AI, the central question remains: How do we design AI products users trust? We’ve seen that trust isn’t just about building advanced algorithms—it’s about design, psychology, transparency, control and lifecycle governance.
Here are your seven core principles summarised:
Understand user attribution: even simple systems can trigger human-like responses (ELIZA).
Build transparency and explainability from day one.
Give users control, reliable outputs and clear error handling.
Manage anthropomorphism and user expectations (the ELIZA effect).
Embed governance, fairness and ethical considerations.
Monitor, iterate and communicate through the product lifecycle.
Educate users and align your brand to build long-term trust.
As a product-minded digital leader, you’re in a unique position to bridge engineering, design and strategic oversight. Use this framework in your next AI product discovery, prototype or scaling conversation. And if you’d like a downloadable checklist or a slide deck to support this article, I’d be happy to help. Let’s make AI products not just intelligent—but trusted.
FAQs
1. What does “trust” mean in the context of AI products?
In AI product design, trust means the user feels confident that the system will perform as expected, their data is handled ethically, the system is transparent about its limitations, and they maintain a sense of control.
2. Is it realistic to expect users to trust AI as they trust humans?
Not exactly. The goal isn’t to replicate human trust but to design systems where the user recognises the machine’s role, understands what it can and cannot do, and feels comfortable engaging with it. Mis‐placed human-like expectations often lead to disappointment (as seen with ELIZA).
3. When should transparency and explainability be introduced in the product lifecycle?
From the earliest discovery and prototype phases. Mapping user mental models, designing how you will signal uncertainty and offering control should be baked into feature design—not added as an afterthought.
4. How does anthropomorphism affect user trust?
Anthropomorphic cues (e.g., human-like avatar, conversational tone) can increase engagement, but they also raise expectations. If the system fails to meet those expectations (e.g., gives a nonsensical answer), trust drops quickly. Balance is key.
5. What are the risks if trust is broken in an AI product?
Loss of adoption, user churn, negative reviews, reputational damage and regulatory scrutiny (especially in sectors like health or finance). Designing for trust is a risk-mitigation strategy as much as a growth lever.
