User control
Who should decide how cautious your AI is?
People want different things from AI. Some value firm safeguards; others want more room to choose. Where should that choice sit?
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A refusal can feel confusing when you do not understand which part of your request caused it.
A refusal can prevent harmful assistance or obstruct a legitimate task. XSTest examines over-refusal; WildGuard distinguishes detecting a refusal from judging harmfulness. Neither high nor low refusal frequency alone determines whether the boundaries are well designed.
A student asks how a historical fraud worked so they can recognize warning signs. Should an assistant explain the mechanism, ask about context or decline? Consider how the same topic changes when the requested help becomes a step-by-step plan to deceive someone.
Explanations and an appeal path help people distinguish a real boundary from a mistake.
Very detailed explanations might also reveal ways to bypass a protection.
Background reading for the tradeoff. Scenarios and discussion questions are editorial examples.
Pairs safe prompts with unsafe contrasts to investigate unnecessary refusals. Historical model results are not current rankings.
Describes prompt harmfulness, response harmfulness and refusal detection as separate tasks.
Sources reviewed 13 September 2026. Product documentation can change. How we use evidence
People want different things from AI. Some value firm safeguards; others want more room to choose. Where should that choice sit?
Verification may reduce some misuse, while creating access and privacy tradeoffs.
Memory can make an assistant more useful. It can also preserve details you shared casually, long after you intended.