Assess whether an agent may take actions without a human gate (2ad07f)

Situation

A customer complaint that a chatbot invented a refund policy put hiring-tool adverse-impact tables in front of vendor-diligence reviewer for AI tools in a bank preparing for a model-risk exam. This AI Governance / Bias and Training Data close is an agent may take from hiring-tool adverse-impact tables, and the live options are Policy or governance breach, Model defect, Dual failure.

Decision

Vendor-diligence reviewer for AI tools in a bank preparing for a model-risk exam must choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact using hiring-tool adverse-impact tables after a customer complaint that a chatbot invented a refund policy.

Hypotheses to test

• Hiring-tool adverse-impact tables reads as Policy or governance breach once a customer complaint that a chatbot invented a refund policy is lined up to the same AI Governance population.
• Hiring-tool adverse-impact tables is closer to Model defect after a customer complaint that a chatbot invented a refund policy; Policy or governance breach would over-claim this Bias and Training Data extract.
• Dual failure is still live in hiring-tool adverse-impact tables for vendor-diligence reviewer for AI tools in a bank preparing for a model-risk exam.
• Hiring-tool adverse-impact tables is missing the fact vendor-diligence reviewer for AI tools needs after a customer complaint that a chatbot invented a refund policy; stop this AI Governance close.

Analysis required

• Walk the model input/output path recorded in hiring-tool adverse-impact tables and mark each hop approved, shadow, or unlogged.
• Verify data provenance and the human-oversight gate vendor-diligence reviewer for AI tools can actually point to.
• Walk the model input/output path recorded in hiring-tool adverse-impact tables and mark each hop approved, shadow, or unlogged.
• For this AI Governance Bias and Training Data file, read hiring-tool adverse-impact tables against a customer complaint that a chatbot invented a refund policy and write the one fact that would move an agent may take for vendor-diligence reviewer for AI tools.

Recommendation

Choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact on this AI Governance / Bias and Training Data packet (hiring-tool adverse-impact tables after a customer complaint that a chatbot invented a refund policy). The follow-on Bias and Training Data action is what vendor-diligence reviewer for AI tools does next: implement the option, assign an owner, and log the missing fact.

Command returns

• Bottom-line AI Governance option on an agent may take, then the evidence in hiring-tool adverse-impact tables, then the action for vendor-diligence reviewer for AI tools
• Hypothesis scorecard against hiring-tool adverse-impact tables: supported / rejected / untestable
• Regulatory or exam hook Bias and Training Data would cite
• Bias and Training Data finding in hiring-tool adverse-impact tables that a second reviewer can re-perform