About Us
AI Trust & Behavioral Science
Other AI advisory practices focus on what people do around AI, but behavioral science explains why they hesitate, resist, or refuse to delegate to it. Education and governance can tell people how a system works or what the rules are, but neither can change the underlying cognitive, emotional, and identity‑based forces that shape real‑world trust.
Behavioral science diagnoses the psychological frictions that block adoption—uncertainty, loss aversion, perceived unfairness, effort costs, and threats to professional identity—none of which are solved by more training or more policy. These patterns determine whether people feel safe, respected, and in control when interacting with AI, and whether they will actually use it when the stakes are high.
Because trust is a behavioral outcome, not a technical one, only behavioral science can surface the hidden drivers behind resistance and design the conditions that make AI feel predictable, fair, and worth delegating to.
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Why Behavioral Science?
Behavioral science is better at diagnosing AI trust issues because it focuses on the psychological forces that shape real‑world adoption—uncertainty, loss aversion, perceived fairness, identity threat, and the instinct to default to human judgment when stakes feel high. These drivers sit underneath the surface and can’t be fixed by more training or more documentation.
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Why Not Education?
Education tells people how an AI system works, but it doesn’t change how safe, respected, or in‑control they feel when using it. Governance sets rules and guardrails, but rules don’t create trust; they only define boundaries.
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Why Not Governance?
Behavioral science underlies AI governance because rules only work when people are motivated to follow them. Governance can set boundaries, but it can’t create trust or change how people feel about delegating decisions to AI. Without behavioral insight, governance stays procedural; with it, governance becomes usable.
Eugene Y. Chan, PhD
Founder & Principal Strategist
Eugene is a behavioral scientist and strategist who helps organizations understand how people interpret signals of trust, credibility, and risk—and how those perceptions shape real‑world decisions. His work focuses on the moments when customers are uncertain, skeptical, or under scrutiny, translating insights from consumer psychology into practical strategies that strengthen confidence, reduce friction, and improve adoption across products, services, and communication.
As both a scholar and practitioner, Eugene bridges rigorous research with real‑world application. His academic work, published in Financial Times Top 50 journals, examines how people evaluate claims, judge credibility, and respond to messages in high‑stakes contexts. This research underpins his consulting practice, where he guides organizations through trust‑critical challenges: designing clearer communication, improving customer experience, and navigating crises where reputation and clarity matter most.
Eugene’s expertise is frequently sought by global media for his ability to explain how people think and decide under pressure. He has authored multiple books on brand resilience and crisis communication, and his insights inform leaders across industries facing complex behavioral and reputational risks.
His work is grounded in a simple belief: organizations make better decisions when they understand the psychology behind customer behavior. By integrating behavioral science with practitioner‑ready design, Eugene helps teams turn scrutiny into resilience and build trust as a durable competitive advantage.
Eugene earned his PhD from the University of Toronto’s Rotman School of Management, MA from the University of Chicago, and AB (Hons) from the University of Michigan. He has taught at Toronto Metropolitan University, Purdue University, Monash University, the University of Technology Sydney, and the University of Ljubljana.