Description

High-quality data labeling is foundational to trustworthy artificial intelligence. Labeling for Trust provides practical, operational guidance for building, managing, and governing data labeling programs that reduce bias, improve model performance, and withstand audit and regulatory scrutiny.

This book focuses on the real-world challenges data teams face when working with internal annotators, external vendors, and offshore labeling suppliers. It explains how labeling decisions affect downstream model risk, fairness outcomes, and compliance obligations, and how weak controls can introduce systemic bias and hidden failures.

Readers will learn how to define labeling standards, select appropriate quality metrics, manage supplier risk, and audit labeling processes over time. The book also addresses human factors in annotation, including consistency, fatigue, and incentive misalignment.

Key topics include:

Designing labeling programs for AI trust and accountability Data quality metrics and acceptance thresholds Bias detection and mitigation at the labeling stage Managing third-party labeling suppliers and contracts Auditing annotation workflows and outcomes

Written for data teams and AI governance leaders, this guide helps organizations treat labeling as a control point—not an afterthought—in responsible AI systems.