Responsible AI Pilot Brief

Problem

Frontline staff spent significant time classifying and summarizing case-intake records. The product team wanted to test whether assisted suggestions could reduce effort without automating final eligibility decisions.

Pilot scope

• Suggest a case classification and short summary for an eligible subset.
• Require trained staff to review, edit, and confirm every output.
• Allow teams to opt out and preserve the original record.
• Exclude final eligibility decisions, enforcement actions, and unsupported document types.

Evaluation

• Reviewer agreement by case type
• Material-error rate and error severity
• Time saved after review and correction
• Opt-out and override patterns
• Privacy, support, and operational incidents

Safeguards

• Human confirmation before use
• Audit logging for suggested and accepted content
• Weekly error review with frontline staff
• Rollback and expansion gates
• Plain-language notice and opt-out

Decision example

Aggregate reviewer agreement reached 84%, but a recurring high-impact error appeared in one category. Expansion paused until the team fixed the source-data mapping and repeated the affected evaluation. The pilot then continued with narrower eligibility criteria.

What this demonstrates

The product decision was not based on one accuracy number. It combined user impact, error severity, staff effort, privacy controls, operational readiness, and a clear owner for escalation.