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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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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. |
format | Online Article Text |
id | pubmed-8365362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT ramchurnsarvapalid trustworthyhumanaipartnerships AT steinsebastian trustworthyhumanaipartnerships AT jenningsnicholasr trustworthyhumanaipartnerships |