In a dedicated Emotion Encoded survey, I engaged 66 participants with a fundamental question regarding the future of technology:
“What would you need to know about an AI tool in order to trust it more?”
The responses highlight that trust in AI is not a default setting—it is a earned currency. People do not simply accept technology because it is "new"; they demand transparency, safety, and respect.
Prioritize clear information about training data. Users care about the foundation of the system to ensure it is fair and representative of reality.
Demand a digestible explanation of the logic and mechanics. They don't need code; they need the "why" behind the decision.
Want transparency regarding failure rates. Accuracy is irrelevant without an honest account of where the system makes mistakes.
Require Human-in-the-Loop (HITL) oversight. Reassurance from human judgment remains essential for high-stakes outcomes.
Cared about the developer's identity, suggesting performance and explanation outweigh brand names.
Expressed they could "never fully trust AI," revealing a psychological baseline of skepticism that technology alone may not solve.
Tech companies often promote Explainable AI (XAI) as a cure-all for public distrust. The reality, however, is more complex. Simply offering algorithmic explanations does not automatically foster a connection.
From a psychological perspective, trust is about reducing uncertainty. Humans are naturally risk-averse. We build trust through consistency and accountability. AI must mirror these human qualities to be accepted into sensitive sectors like healthcare or law.
"Ultimately, building trust in AI is not just a technical challenge, but a psychological one. It is built through openness and respecting the human need for clarity and control."