"As AI chat interfaces become more popular, users increasingly rely on AI outputs to make decisions. Without explanations, AI systems are black boxes. Explaining to people how an AI system has reached a particular output helps users form accurate mental models, prevents the spread of misinformation, and helps users decide whether to trust an AI output. However, the explanations currently offered by large language models (LLMs) are often inaccurate, hidden, or confusing."
"Currently, explainable AI is limited by technical complexity. The technical reality is that modern AI models are so complex that even AI engineers cannot always accurately trace the reasons behind an output. Right now, we do not have AI that can fully and transparently explain everything it does. The technical details of building accurate, explainable models are ongoing conversations in the industry."
"Yet, when AI chatbots confidently present their outputs, users may place undeserved trust in these answers. Many AI chatbots attempt to provide at least basic explanations (such as source) for their answers, but often these explanations are inaccurate or hallucinated. Users who are not aware of the limitations see a confident and plausible explanation and automatically trust the output. The more users trust AI, the more likely they are to use AI tools without question."
Explainable AI aims to make model reasoning transparent and understandable so people can evaluate outputs. Modern large language models are technically complex, and engineers cannot always trace why a model produced a given output. Many chatbots present confident textual explanations that are inaccurate, hidden, or hallucinated, which can lead users to form incorrect mental models and place undeserved trust in outputs. Accurate, clear explanations help prevent misinformation and support user decisions about trust. UX teams designing chat interfaces must focus on how textual explanations are presented, clarify limitations, and avoid implying false certainty.
Read at Nielsen Norman Group
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