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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Verification may reduce some misuse, while creating access and privacy tradeoffs.
Identity verification can add friction, collect sensitive information and exclude people who lack accepted documents. It also does not establish that an identified person will act safely. Consider whether the risk can be addressed through narrower permissions or a less intrusive check before requiring a real-world identity.
Compare anonymous access to an educational assistant with authority to act through an organizational account. A system may need authentication for the second without needing a copy of every user’s identity document for the first.
Additional accountability can be proportionate for powerful capabilities.
Identity collection creates another sensitive dataset and may exclude legitimate users.
Background reading for the tradeoff. Scenarios and discussion questions are editorial examples.
A framework for identifying, measuring and managing generative AI risks across the system lifecycle.
Connects safety, privacy, inclusion, development and children’s participation in AI design.
Practical guidance on tool permissions, memory isolation, oversight and agent failure handling.
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?
A refusal can feel confusing when you do not understand which part of your request caused it.
Memory can make an assistant more useful. It can also preserve details you shared casually, long after you intended.