Artificial intelligence can be highly useful yet produce confidently stated but incorrect outputs. Skepticism requires focusing on alignment between the human problem and the machine's interpretation, not just questioning content. Begin by defining the problem and explicitly asking what problem is being solved by the user versus the AI. Identify misalignments such as models predicting statistically likely sentences rather than addressing leadership development. Check and test assumptions about evidence relevance, accuracy, sufficiency, and data recency or bias. Treat AI confidence as form, not proof, and avoid dependence created by design features that encourage longer interactions.
Artificial intelligence is quickly becoming the colleague we never asked for: answering our questions, drafting our documents, screening our résumés, and nudging our decisions. But while AI can be dazzlingly useful, it can also be dead wrong. The challenge is not simply whether to use AI, but how to think skeptically about the results it produces. Skepticism is not a natural reflex.
Skepticism is both broader and narrower than critical thinking. It is both an approach to evaluating information and a mindset. Skepticism is not cynicism. Skeptics do not automatically believe that all information is false. Nor do they automatically believe that all information - especially when delivered by AI - is automatically accurate. Here are five steps to improve your productivity with AI. Priyanka Shrivastava and I are working to validate this process with survey data and interviews.
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