"In my last piece, we established a foundational truth: for users to adopt and rely on AI, they must trust it. We talked about trust being a multifaceted construct, built on perceptions of an AI's Ability, Benevolence, Integrity, and Predictability. But what happens when an AI, in its silent, algorithmic wisdom, makes a decision that leaves a user confused, frustrated, or even hurt?"
"Our conversation now must evolve from the why of trust to the how of transparency. The field of Explainable AI (XAI), which focuses on developing methods to make AI outputs understandable to humans, has emerged to address this, but it's often framed as a purely technical challenge for data scientists. I argue it's a critical design challenge for products relying on AI. It's our job as UX professionals to bridge the gap between algorithmic decision-making and human understanding."
User trust in AI depends on perceptions of ability, benevolence, integrity, and predictability. Opaque or surprising AI decisions erode trust and can confuse, frustrate, or harm users in high-stakes contexts like lending, media recommendations, and hiring. Explainable AI (XAI) focuses on answering "Why?" to make AI outputs understandable by revealing reasoning or contributing factors. Designers and UX professionals must translate technical XAI methods into concrete, usable interface patterns. Practical research, mockups, and design patterns enable products to surface explanations that restore comprehension and support reliable user adoption.
Read at Smashing Magazine
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