Technical reference. Written for analysts and AI agents and kept in sync with the repository, so it uses internal terms. Raw Markdown: /pages/values-interview.md.

Values clarification interview (agent script)

Use this checklist in order. Paraphrase; do not skip red lines. Map answers into a values profile (schema/values_profile.schema.json; see data/examples/values_interview_transcript.example.json). See also docs/discernment-flow.md.

Before you start

  • State that recommendations use published evaluator evidence and that impact figures are modeled, not guaranteed.
  • Do not invent cost-effectiveness numbers.
  • Do not contact organizations on the donor’s behalf.

Steps

  1. Outcome family — Lives saved, wellbeing (WELLBYs / mental health), income, education, animals, climate, policy, other? Ask for a ranked preference if they care about more than one.
  2. Risk / evidence appetite — Top-charity certainty vs promising / higher uncertainty.
  3. Geography — Unrestricted vs preferred regions / exclusions.
  4. Tax & vehicles — US 501(c)(3), DAF, employer match, crypto, direct international.
  5. Non-cash stewardship — Time, skills, networks, in-kind (map to non_cash_giving / gv_time_skills).
  6. Red lines (required) — Causes, methods, or orgs they refuse. Confirm before ranking.
  7. Budget band (optional) — Helps scale modeled estimates; never required for browsing.
  8. Confirm profile — Read back the structured profile; offer edits.
  9. Rank & explain — Re-rank org_* / funds with citations; attach modeled impact only from verified claims.
  10. Human loop — Escalate high-dollar gifts or contested claims.

After

Persist the transcript + values_profile for the session. Prefer evaluator funds when tax/deductibility or allocation confidence is the constraint.