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Trustworthy human-AI partnerships

In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organizations trust their machine counterparts ind...

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Detalles Bibliográficos
Autores principales: Ramchurn, Sarvapali D., Stein, Sebastian, Jennings, Nicholas R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365362/
https://www.ncbi.nlm.nih.gov/pubmed/34430804
http://dx.doi.org/10.1016/j.isci.2021.102891
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author Ramchurn, Sarvapali D.
Stein, Sebastian
Jennings, Nicholas R.
author_facet Ramchurn, Sarvapali D.
Stein, Sebastian
Jennings, Nicholas R.
author_sort Ramchurn, Sarvapali D.
collection PubMed
description In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organizations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry.
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spelling pubmed-83653622021-08-23 Trustworthy human-AI partnerships Ramchurn, Sarvapali D. Stein, Sebastian Jennings, Nicholas R. iScience Review In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organizations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry. Elsevier 2021-07-24 /pmc/articles/PMC8365362/ /pubmed/34430804 http://dx.doi.org/10.1016/j.isci.2021.102891 Text en © 2021. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review
Ramchurn, Sarvapali D.
Stein, Sebastian
Jennings, Nicholas R.
Trustworthy human-AI partnerships
title Trustworthy human-AI partnerships
title_full Trustworthy human-AI partnerships
title_fullStr Trustworthy human-AI partnerships
title_full_unstemmed Trustworthy human-AI partnerships
title_short Trustworthy human-AI partnerships
title_sort trustworthy human-ai partnerships
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365362/
https://www.ncbi.nlm.nih.gov/pubmed/34430804
http://dx.doi.org/10.1016/j.isci.2021.102891
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