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Trusting machines? Cross-sector lessons from healthcare & security: conference report

Trusting machines? Cross-sector lessons from healthcare & security: conference report
Trusting machines? Cross-sector lessons from healthcare & security: conference report
RUSI and UKRI TAS Hub–Trustworthy Autonomous Systems Hub –have presented Trusting Machines? Cross-sector Lessons from Healthcare and Security. The conference was held between June 30th and July 2, 2021. Over three days of discussions, the conference was a forum to bring together academic experts, policy leaders and industry professionals to discuss how autonomous systems can be responsibly integrated into the healthcare and security sectors. With a focus on building trustworthy autonomous systems, the conference covered topics related to both healthcare and security research and identified development areas. The conference addressed a variety of case studies and current research challenges. Delegates presented and discussed the key global issues facing AI development, highlighting the competitive aspects, risks, and opportunities that both nations and organisations will face in the years and decades to come. As a result, keynote sessions, project presentations and workshops have been presented in accordance with the conference scope and discussed challenges, opportunities, and research problems of building trustworthy autonomous systems. The following report is intended for all those interested in the current challenges and constraints involved in AI development within these sectors. Additionally, the conference features numerous discussions regarding the potential for cross-sector lessons, therefore we welcome readers from the broader AI community who are seeking a concise summary of the global affairs in AI development and implementation.
University of Southampton
Naiseh, Mohammad
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Clark, Jediah
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Divband Soorati, Mohammad
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Bossens, David
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Sylvaine Tuncer
Naiseh, Mohammad
ab9d6b3c-569c-4d7c-9bfd-61bbb8983049
Clark, Jediah
5d82ac6c-58be-4366-9b11-5e3179d85b33
Divband Soorati, Mohammad
35fe6bbb-ce52-4c21-a46e-9bb0e31d246c
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873

Naiseh, Mohammad, Clark, Jediah, Divband Soorati, Mohammad and Bossens, David , Sylvaine Tuncer (2021) Trusting machines? Cross-sector lessons from healthcare & security: conference report Southampton. University of Southampton 20pp. (doi:10.5257/SOTON/P0134).

Record type: Monograph (Project Report)

Abstract

RUSI and UKRI TAS Hub–Trustworthy Autonomous Systems Hub –have presented Trusting Machines? Cross-sector Lessons from Healthcare and Security. The conference was held between June 30th and July 2, 2021. Over three days of discussions, the conference was a forum to bring together academic experts, policy leaders and industry professionals to discuss how autonomous systems can be responsibly integrated into the healthcare and security sectors. With a focus on building trustworthy autonomous systems, the conference covered topics related to both healthcare and security research and identified development areas. The conference addressed a variety of case studies and current research challenges. Delegates presented and discussed the key global issues facing AI development, highlighting the competitive aspects, risks, and opportunities that both nations and organisations will face in the years and decades to come. As a result, keynote sessions, project presentations and workshops have been presented in accordance with the conference scope and discussed challenges, opportunities, and research problems of building trustworthy autonomous systems. The following report is intended for all those interested in the current challenges and constraints involved in AI development within these sectors. Additionally, the conference features numerous discussions regarding the potential for cross-sector lessons, therefore we welcome readers from the broader AI community who are seeking a concise summary of the global affairs in AI development and implementation.

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Published date: 2 July 2021

Identifiers

Local EPrints ID: 451284
URI: http://eprints.soton.ac.uk/id/eprint/451284
PURE UUID: cd687a71-ec79-470b-b4d1-5fbc0042d844
ORCID for Mohammad Naiseh: ORCID iD orcid.org/0000-0002-4927-5086
ORCID for Jediah Clark: ORCID iD orcid.org/0000-0002-1356-2462
ORCID for Mohammad Divband Soorati: ORCID iD orcid.org/0000-0001-6954-1284
ORCID for David Bossens: ORCID iD orcid.org/0000-0003-1924-5756

Catalogue record

Date deposited: 16 Sep 2021 16:31
Last modified: 14 Oct 2021 02:03

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Contributors

Author: Mohammad Naiseh ORCID iD
Author: Jediah Clark ORCID iD
Author: Mohammad Divband Soorati ORCID iD
Author: David Bossens ORCID iD
Corporate Author: Sylvaine Tuncer

University divisions

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