The Alan Turing Institute has recently released Workbook No. 5, titled “Responsible Data Stewardship in Practice,” as part of its AI Ethics and Governance in Practice Programme. This release follows the UK’s National AI Strategy recommendation from 2021. The workbook is designed to provide resources and training to help teams ethically manage data throughout the AI project lifecycle, promoting responsible data practices. The goal is to ensure that AI models are developed and deployed in an ethical and responsible manner, aligning with the broader objectives of the National AI Strategy.
The AI Ethics and Governance in Practice Programme comprises eight workbooks and a digital platform, offering tools, training, and support for adopting a Process-Based Governance (PBG) Framework for responsible AI innovation. This framework emphasizes key principles such as sustainability, safety, accountability, fairness, explainability, and data stewardship. Each workbook in the series focuses on a specific domain and uses case studies to promote ethical reflection and illustrate key concepts.
The “Responsible Data Stewardship in Practice” workbook provides a detailed guide for teams on how to ethically manage data. It includes instructions on how to apply responsible data practices at various stages of an AI project’s lifecycle. These stages include data planning, creation, curation, extraction, procurement, analysis, preprocessing, feature engineering, usage, and decommissioning. The workbook emphasizes the importance of maintaining data integrity, quality, and privacy throughout these stages.
One of the key components of responsible data stewardship highlighted in the workbook is the creation of Data Factsheets. These factsheets are tools to facilitate the uptake of best practices for data management, ensuring that data integrity, quality, protection, and privacy are maintained across the AI project workflow. The Data Factsheets outline comprehensive records of data lineage and include qualitative input from team members about the determinations made regarding the different components of responsible data stewardship.
The workbook also stresses the importance of engaging with stakeholders and maintaining transparency in data practices. Effective data stewardship involves regular communication and engagement with internal staff, AI suppliers, customers, and regulators to build trust and ensure that all relevant parties are informed about AI practices and policies. This approach helps in documenting the tools, approaches, and policies used, which should be thoroughly discussed with core teams and stakeholders.
Furthermore, the workbook discusses the significance of incorporating ethical and safety principles into public sector algorithmic systems. This is in line with the guidance published by the UK government in collaboration with the Office for AI and the Government Digital Service. The guidance was updated and expanded from 2021 to 2023 with support from the Office for AI, the Engineering and Physical Sciences Research Council, and various public sector bodies.
The Alan Turing Institute’s Public Policy Programme, established in May 2018, aims to develop research, tools, and techniques that help governments innovate with data-intensive technologies and improve the quality of public services. The programme works alongside policymakers to explore how data science and AI can inform public policy and enhance public service delivery. The “Responsible Data Stewardship in Practice” workbook is a part of this broader initiative to ensure that AI technologies are developed and deployed ethically, safely, and responsibly.
In summary, the release of the “Responsible Data Stewardship in Practice” workbook by The Alan Turing Institute is a significant step towards promoting ethical AI practices. It provides practical guidance and tools for teams to manage data responsibly, ensuring that AI models are developed in line with ethical standards and regulatory requirements. This initiative is expected to contribute significantly to the responsible and trustworthy development of AI technologies in the public sector.
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