Building trust in opaque systems

"How do we know when to trust what someone tells us? In person conversations give us many subtle cues we might pick up on, but when they happen with AI system designed to sound perfectly human, we lose any sort of frame of reference we may have. With every new model, conversational AI sounds more and more genuinely intelligent and human-like, so much so that every day, millions of people chat with these systems as if talking to their most knowledgeable friend."

"From a design perspective, they're very successful in the way they feel natural, authoritative and even empathetic, but this very naturalness becomes problematic as it makes it hard to distinguish when outputs are true or simply just plausible. This creates exactly the setup for misplaced trust: trust works best when paired with critical thinking, but the more we rely on these systems, the worse we get at it, ending up in this odd feedback loop that's surprisingly difficult to escape."

"Traditional software is straightforward - click this button, get that result. AI systems are something else entirely because they're unpredictable as they can make new decisions based on their training data. If we ask the same question twice we might get completely different wording, reasoning, or even different conclusions each time. How this thing thinks and speaks in such human ways, feels like magic to many users."

Conversational AI increasingly sounds human, natural, authoritative, and empathetic, prompting millions of people to interact with systems as if with knowledgeable friends. Natural-sounding responses obscure whether outputs are accurate or merely plausible, producing conditions for misplaced trust. AI responses are unpredictable and can vary in wording, reasoning, and conclusions even for repeated questions because models select statistically probable word sequences from training data. The perceived intelligence feels like magic when users lack understanding of system processes. Reliance on these systems reduces critical thinking, creating a feedback loop where trust increases while scrutiny decreases, making it harder to detect errors or hallucinations.

Read at Medium