Topics Archive - Page 127 of 331

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CAHAI feasibility study of a legal framework on AI Copy

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Last Updated: 15 Dec 2022

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Towards transparent and explainable AI: Workshop to inform ISO/IEC standards development

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Last Updated: 7 Dec 2022

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Submit your questions: Webinar on standards for transparent and explainable AI

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Our upcoming webinar Towards Transparent and Explainable AI: The Current Standardisation Landscape will provide an overview of the most prominent…

Last Updated: 7 Dec 2022

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Towards Transparent and Explainable AI: The Current Standardisation Landscape

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Last Updated: 25 Nov 2022

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The Digital World: Shaping global standards for Artificial Intelligence

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Last Updated: 27 Oct 2022

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Using machine learning in diagnostic services: a report with recommendations from CQC’s regulatory sandbox

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Last Updated: 12 Oct 2022

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Understanding UK artifcial intelligence R&D commercialisation and the role of standards

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Last Updated: 12 Oct 2022

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Data-driven healthcare: regulation and regulators

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Last Updated: 12 Oct 2022

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Understanding the UK AI labour market: 2020

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Last Updated: 12 Oct 2022

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Data foundations and AI adoption in the UK private and third sectors

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Last Updated: 12 Oct 2022

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Content Type

CAHAI feasibility study of a legal framework on AI Copy

Towards transparent and explainable AI: Workshop to inform ISO/IEC standards development

Submit your questions: Webinar on standards for transparent and explainable AI

Towards Transparent and Explainable AI: The Current Standardisation Landscape

The Digital World: Shaping global standards for Artificial Intelligence

Using machine learning in diagnostic services: a report with recommendations from CQC’s regulatory sandbox

Understanding UK artifcial intelligence R&D commercialisation and the role of standards

Data-driven healthcare: regulation and regulators

Understanding the UK AI labour market: 2020

Data foundations and AI adoption in the UK private and third sectors

Complete transparency, complete simplicity: how can the public sector be meaningfully transparent about algorithmic decision making?

Review into bias in algorithmic decision-making

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