A county human-services agency is configuring a GenAI tool intended to help caseworkers draft summaries and identify relevant policy sections during benefits-eligibility reviews. What guardrail should leadership set regarding the tool's role in the eligibility process?

Restricting the GenAI tool to decision-support functions, like drafting summaries and surfacing relevant policy sections, while requiring a caseworker to independently review and issue the actual eligibility determination, keeps the AI in an assistive role for a high-stakes decision that materially affects an applicant's access to benefits. Allowing the tool to issue determinations directly whenever a confidence score clears a threshold treats a statistical confidence measure as equivalent to a substantive eligibility judgment, when in fact a model can be confidently wrong about a case with unusual or ambiguous facts a caseworker would catch. Carving out an exception for applicants who haven't requested a reasonable accommodation creates an arbitrary and potentially discriminatory dividing line unrelated to whether the case itself is straightforward, and accommodation status isn't a sound proxy for eligibility complexity. Allowing automated determinations for cases below a dollar-value threshold assumes smaller benefit amounts always carry lower risk of harm, but a small benefit can still be essential to the applicant receiving it, and a wrongly denied small benefit is still a wrongful denial. A scope caveat: decision-support tools still need their own monitoring, since even non-final outputs like a flawed policy citation can mislead a caseworker's judgment if unchecked. A concrete check: confirm the system's workflow technically requires a caseworker's affirmative sign-off before any eligibility outcome is finalized and communicated to the applicant.