Module Contents
Introduction to this Module
The purpose of this workbook is to introduce participants to the principle of Fairness. Reaching consensus on a commonly accepted definition for fairness has long been a central challenge in AI ethics and governance. There is a broad spectrum of views across society on what the concept of fairness means and how it should best be put to practice. In this workbook, we tackle this challenge by exploring how a context-based and society–centred approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow. This workbook is divided into two sections, Key Concepts and Activities.
KEY CONCEPTS
This section provides content for workshop participants and facilitators to engage with prior to attending each workshop. It covers the following topics and provides case studies aimed to support a practical understanding of fairness and bias within AI systems.
ACTIVITIES
This section contains instructions for group-based activities (each corresponding to a section in the Key Concepts). These activities are intended to increase understanding of Key Concepts by using them.
Case studies within the AI Ethics and Governance in Practice workbook series are grounded in public sector use cases, but do not reference specific AI projects.
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