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Ethics

Disclosure and Deception

When an AI clone speaks on someone's behalf, the recipient has a legitimate interest in knowing it. The hard part is what counts as adequate disclosure.

Three positions

Position 1

Strict disclosure

Every clone-generated interaction is labeled at the start and at any point the user could reasonably forget. No exceptions.

Position 2

Context-aware disclosure

Disclosure intensity scales with stakes. Casual scheduling can be one-time; medical advice or political speech requires repeated, persistent labels.

Position 3

Functional disclosure

Provenance metadata embedded in the artifact is sufficient. Human-visible labels create alarm fatigue and are ignored.

Best practices

  • Stake-weighted labels. The higher the consequence, the more conspicuous the label.
  • Provenance plus visible label, not one or the other.
  • User-facing log. Recipients can see at any time which interactions were clone-mediated.