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Good practice for development and use
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AI risk management framework
NIST is developing a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI). The…
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A taxonomy and terminology of adversarial machine learning
This NIST Interagency/Internal Report (NISTIR) is intended as a step toward securing applications of Artificial Intelligence (AI), especially against adversarial…
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From principles to practice: an interdisciplinary framework to operationalise AI ethics
Artificial intelligence (AI) increasingly pervades all areas of life. To seize the opportunities this technology offers society, while limiting its…
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AI, human rights, democracy and the rule of law: a primer
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Connected and automated vehicles: a review of the UK’s legislation and good practice
The white paper provides an overview of the CCAV Code of Practice (CoP): Automated Vehicle Trialling, BSI’s CAV standards programme,…
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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
A remarkable time of human promise has been ushered in by the convergence of the ever-expanding availability of big data,…
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Data justice in practice guides
The Data Justice in Practice Guides are a series of three guides for policymakers, impacted communities, and developers. The guides…
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Advancing data justice
In 2021, the Global Partnership on AI chose The Alan Turing Institute to lead a project they had begun titled…
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The ethics of artificial intelligence: issues and initiatives
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Good practice in training set preparation for marine navigation systems
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Using machine learning in diagnostic services: a report with recommendations from CQC’s regulatory sandbox
This report presents the findings from the Care Quality Commission’s (CQC’s) regulatory sandbox pilot. Regulatory sandboxing is a way of…
