Reaching consensus on a commonly accepted definition of AI 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.
We begin by exploring how, despite the plurality of understandings about the
meaning of fairness, priorities of equality and non-discrimination have come to
constitute the broadly accepted core of its application as a practical
principle. We focus on how these priorities manifest in the form of equal
protection from direct and indirect discrimination and from discriminatory
harassment. These elements form ethical and legal criteria based upon which
instances of unfair bias and discrimination can be identified and mitigated
across the AI project workflow.
We then take a deeper dive into how the different contexts of the AI project
lifecycle give rise to different fairness concerns. This allows us to identify
several types of AI Fairness (Data Fairness, Application Fairness, Model Design
and Development Fairness, Metric-Based Fairness, System Implementation
Fairness, and Ecosystem Fairness) that form the basis of a multi-lens approach
to bias identification, mitigation, and management. Building on this, we
discuss how to put the principle of AI Fairness into practice across the AI
project workflow through Bias Self-Assessment and Bias Risk Management as well
as through the documentation of metric-based fairness criteria in a Fairness
Position Statement.

AI Fairness 领域存在多种理解和多样化的公平概念，对此，本文提出了一种基于上下文和以社会为中心的方法来帮助项目团队更好地识别、减轻和处理人工智能项目流程中出现的不公平偏见和歧视，并讨论了如何通过自我评估、风险管理和公平准则的文档化来实现 AI 公平原则。