Answer capsule
The framework separates application types and covers disclosure, system limits, coaching behavior, assurance, testing, privacy, security, accessibility, and referral concerns.
What the source establishes
- ICF's framework contains six domains and thirteen sets of requirements.
- It distinguishes scheduling, data-processing, interactive, and conversational uses and whether technology assists a coach or coaches a client directly.
- The framework requires clear nonhuman disclosure and system-limit communication for relevant client-facing systems.
Decision implication
The framework enables a control-by-control comparison that is more defensible than treating every product with a chat interface as the same kind of coach.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Evidence to inspect
Ask vendors to map each deployed application type to relevant requirements and provide test evidence, named owners, version dates, exceptions, and unresolved gaps.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Boundary and caveat
Self-claimed alignment is not ICF certification and should not be converted into a score without reviewing underlying evidence and deployment scope.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
What to do next
Use the framework as the base of a request-for-evidence package and preserve responses against the exact product version and configuration.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Decision test
Ask whether the source changes the decision itself, the evidence required, the implementation sequence, or only the language used to describe an existing capability. Record which claims are directly supported, which are provider statements, which require an independent test, and which remain unknown. A source-linked review should make uncertainty easier to see, not bury it inside a blended score.
Questions to take into review
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