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Overview and commentary of the CDEI's extended roadmap to an effective AI assurance ecosystem

Overview of attention for article published in Frontiers in Artificial Intelligence, August 2022
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Title
Overview and commentary of the CDEI's extended roadmap to an effective AI assurance ecosystem
Published in
Frontiers in Artificial Intelligence, August 2022
DOI 10.3389/frai.2022.932358
Pubmed ID
Authors

Ethan Barrance, Emre Kazim, Airlie Hilliard, Markus Trengove, Sara Zannone, Adriano Koshiyama

Abstract

In recent years, the field of ethical artificial intelligence (AI), or AI ethics, has gained traction and aims to develop guidelines and best practices for the responsible and ethical use of AI across sectors. As part of this, nations have proposed AI strategies, with the UK releasing both national AI and data strategies, as well as a transparency standard. Extending these efforts, the Centre for Data Ethics and Innovation (CDEI) has published an AI Assurance Roadmap, which is the first of its kind and provides guidance on how to manage the risks that come from the use of AI. In this article, we provide an overview of the document's vision for a "mature AI assurance ecosystem" and how the CDEI will work with other organizations for the development of regulation, industry standards, and the creation of AI assurance practitioners. We also provide a commentary of some key themes identified in the CDEI's roadmap in relation to (i) the complexities of building "justified trust", (ii) the role of research in AI assurance, (iii) the current developments in the AI assurance industry, and (iv) convergence with international regulation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 26%
Researcher 2 11%
Lecturer 1 5%
Lecturer > Senior Lecturer 1 5%
Student > Ph. D. Student 1 5%
Other 1 5%
Unknown 8 42%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 21%
Social Sciences 2 11%
Environmental Science 1 5%
Computer Science 1 5%
Business, Management and Accounting 1 5%
Other 2 11%
Unknown 8 42%