---
name: tai-ch142-bias-in-deployment-feedback-loops-and-productization
description: 'Apply chapter 142 of Testing AI, Bias in Deployment, Feedback Loops, and Productization, 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 bias in deployment, feedback loops, and productization.'
---

# Bias in Deployment, Feedback Loops, and Productization

Skill name: `tai-ch142-bias-in-deployment-feedback-loops-and-productization`

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

## Purpose

Even a well-tested model can become biased when the product around it changes who is seen,
measured, and rewarded.

## 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

Deployment changes the system. A model that performed acceptably in offline evals may behave
differently when exposed to real users, market incentives, content creators, attackers, and
feedback loops.

## 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, bias testing after launch should include exposure metrics, feedback-loop
audits, slice dashboards, drift detection, intervention tests, and governance for when business
metrics conflict with fairness or safety metrics.