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AI Ethics and Governance in Practice: AI Fairness in Practice
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display title: AI Ethics and Governance in Practice: AI Fairness in Practice
has best practice: This workbook discusses 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.
has good practice and misconduct: AI Ethics
has link: https://www.turing.ac.uk/sites/default/files/2023-12/aieg-ati-fairness_1.pdf
has location: United Kingdom
has timepoint: 2023
has virtue and value: Accountability
has virtue and value: Responsible Data Stewardship
has virtue and value: Safety
has virtue and value: Explainability
has virtue and value: Fairness
has virtue and value: Sustainability
has virtue and value: Responsible Research and Innovation
important because: 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. 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.
important for: Public Authority Representative
important for: Civil Society / NGO Representative
important for: Technology Developer / Engineer
involves: The Alan Turing Institute
is about: In 2021, the UK's National AI Strategy recommended that UK Government’s official Public Sector Guidance on AI Ethics and Safety be transformed into a series of practice-based workbooks. The result is the [https://www.turing.ac.uk/research/research-projects/ai-ethics-and-governance-practice AI Ethics and Governance in Practice Programme]. This series of eight workbooks provides end-to-end guidance on how to apply principles of AI ethics and safety to the design, development, deployment, and maintenance of AI systems. It provides public sector organisations with a Process Based Governance (PBG) Framework designed to assist AI project teams in ensuring that the AI technologies they build, procure, or use are ethical, safe, and responsible. This workbook explores 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.
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title: AI Ethics and Governance in Practice: AI Fairness in Practice
wiki page creation date: 2024-04-10T14:04:28Z
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Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector
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Responsible Use of AI in Research: Balancing Innovation and Integrity
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