The document focuses on applying fairness in AI, addressing how to identify and mitigate bias and discrimination. It introduces concepts like fairness, Public Sector Equality Duty, and the principle of Discriminatory Non-Harm. The workbook suggests practical steps like Bias Self-Assessment, Bias Risk Management, and preparing a Fairness Position Statement, covering different aspects of fairness throughout the AI project lifecycle.

This workbook introduces the concept of fairness in AI, emphasizing the need for a context-based and society-centred approach to tackle biases and discrimination throughout the AI project lifecycle. The workbook on AI fairness outlines the crucial aspects of incorporating fairness throughout the AI project lifecycle. It emphasizes understanding biases, both in data and in societal contexts, to mitigate unfair practices. By adopting a multi-faceted approach to fairness, incorporating various definitions and metrics, and considering legal and ethical standards, the workbook guides towards more equitable AI applications. It stresses the importance of integrating diverse perspectives and addressing the complexities of biases to ensure AI systems are developed and deployed responsibly.