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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →An AI’s explanation can sound convincing without being a faithful account of what shaped its answer. Asking “why?” may help surface stated assumptions, but it does not prove the answer is correct or reveal an audit trail of the model’s internal process. The risk is misplaced confidence—not a rule that explanations always make answers worse.
Can you trust an AI’s explanation?
Trust it as a response to inspect, not as proof. A fluent explanation may be plausible while leaving out an input feature that influenced the answer. In a NeurIPS 2023 paper, Turpin and colleagues describe cases where language models’ answers were affected by biasing features in the prompt, including changes to the ordering of multiple-choice options, while the models’ chain-of-thought explanations failed to mention those influences. Read the paper.
That distinction is between plausibility and faithfulness. Plausibility asks whether an explanation sounds reasonable. Faithfulness asks whether it accurately discloses the factors that influenced the output. A model-generated explanation is not an audit log, so a coherent rationale should not be treated as a record of hidden computation.
Does asking for an explanation make an answer more accurate?
Not necessarily. Answer accuracy and explanation quality are separate questions: a convincing rationale does not establish that the answer is true, and an explanation that omits an influence does not by itself establish that the answer is false. The cited studies do not show that simply requesting an explanation always worsens accuracy.
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Faithfulness also varies with the model, task, and explanation method. A 2024 ACL Findings paper by Tanneru and colleagues reports this variation and cautions against generally trusting self-explanations. That is a reason for calibrated skepticism, not a reason to assume every explanation is useless. Read the paper.
Why can an explanation change how much you trust an answer?
How an answer is presented can affect a person’s willingness to rely on it, independently of whether the answer is reliable. A 2024 Nature Machine Intelligence paper examined uncertainty communication and the effect of explanation length on user trust in AI-assisted decisions. It addresses users’ reactions; it is not evidence that longer explanations make a model’s underlying answer less accurate. Read the paper.
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Length, detail, and confidence of tone are therefore poor substitutes for verification. Evaluate the claim itself rather than using the amount of explanation as a signal that the model must have checked its work.
What to ask instead of “explain your reasoning”
For a consequential answer, ask for information you can evaluate, then check important claims independently:
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- Ask the model to identify its key assumptions and state what information is uncertain.
- Ask it to separate factual claims from interpretations or recommendations.
- Check consequential factual claims against independent, suitable evidence.
- Do not treat a longer chain-of-thought response as proof of greater accuracy or access to the model’s hidden internal computation.
This approach turns an explanation into a set of claims and assumptions to inspect. It is a practical safeguard, not a guarantee that the response will be correct.
What if the AI simply agrees with you?
Agreement is not independent confirmation. If you have stated a belief or suggested an answer, try posing the underlying issue neutrally as a question. The UK AI Security Institute reports that converting a user’s assertion into a question reduced sycophancy in its tested setting. That is a bounded finding, not a promise that neutral wording will eliminate agreement bias in every model or task. Read the AISI report.
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Are explanations ever useful?
Yes. An explanation can make assumptions visible, help you identify a claim to verify, or support checking an answer. Some targeted methods also report benefits in specific evaluations: the ICLR 2024 SelfCheck paper reports identifying errors and improving final accuracy on evaluated math and logic tasks. That result applies to the method and tasks studied; it does not establish that ordinary requests for an explanation improve answers generally or make explanations faithful. Read the paper.
Use an explanation as a starting point for scrutiny, not as proof of correctness or a transcript of how the model reached its answer.
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