---
name: tai-ch106-governance-for-ai-quality
description: 'Apply chapter 106 of Testing AI, Governance for AI Quality, as a workflow for evaluating AI and non-deterministic systems. Use for test planning, eval design, quality review, release evidence, examples, or coaching related to governance for ai quality.'
---

# Governance for AI Quality

Skill name: `tai-ch106-governance-for-ai-quality`

Based on **Testing AI: Engineering Confidence in AI Systems** by **Jason Arbon**.

## Purpose

AI quality needs ownership, decision rights, audit trails, and escalation paths before the
incident happens.

## Use This Workflow

- Identify the AI behavior or release decision being evaluated.
- Define realistic cases, slices, unacceptable outcomes, and evidence needed for confidence.
- Choose measurements that match the risk: rubric scores, samples, intervals, traces, human review, deterministic checks, or production monitors.
- Report uncertainty, severe failures, and decision impact instead of only a pass/fail result.

## Key Guidance

Governance is how a team decides who owns quality decisions. It is not only a compliance
exercise. It is operational clarity. AI systems cross boundaries: product, engineering, data,
safety, legal, security, support, and vendors. Without governance, everyone assumes someone else
checked the hard part.

## Apply The Approach

Create representative cases, score them with explicit criteria, review severe failures separately, report uncertainty, and connect the evidence to a concrete decision.

## Expert Notes

At expert level, governance connects eval provenance, incident response, access control, vendor
management, and release gates. The audit trail should show who approved what evidence under
which constraints.