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Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations
In the digital age, saving and accumulating large amounts of digital data is a common phenomenon. However, saving does not only consume energy, but may also cause information overload and prevent people from staying focused and working effectively. We present and systematically examine an explanator...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727201/ https://www.ncbi.nlm.nih.gov/pubmed/36504690 http://dx.doi.org/10.3389/frai.2022.919534 |
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author | Göbel, Kyra Niessen, Cornelia Seufert, Sebastian Schmid, Ute |
author_facet | Göbel, Kyra Niessen, Cornelia Seufert, Sebastian Schmid, Ute |
author_sort | Göbel, Kyra |
collection | PubMed |
description | In the digital age, saving and accumulating large amounts of digital data is a common phenomenon. However, saving does not only consume energy, but may also cause information overload and prevent people from staying focused and working effectively. We present and systematically examine an explanatory AI system (Dare2Del), which supports individuals to delete irrelevant digital objects. To give recommendations for the optimization of related human-computer interactions, we vary different design features (explanations, familiarity, verifiability) within and across three experiments (N(1) = 61, N(2) = 33, N(3)= 73). Moreover, building on the concept of distributed cognition, we check possible cross-connections between external (digital) and internal (human) memory. Specifically, we examine whether deleting external files also contributes to human forgetting of the related mental representations. Multilevel modeling results show the importance of presenting explanations for the acceptance of deleting suggestions in all three experiments, but also point to the need of their verifiability to generate trust in the system. However, we did not find clear evidence that deleting computer files contributes to human forgetting of the related memories. Based on our findings, we provide basic recommendations for the design of AI systems that can help to reduce the burden on people and the digital environment, and suggest directions for future research. |
format | Online Article Text |
id | pubmed-9727201 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97272012022-12-08 Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations Göbel, Kyra Niessen, Cornelia Seufert, Sebastian Schmid, Ute Front Artif Intell Artificial Intelligence In the digital age, saving and accumulating large amounts of digital data is a common phenomenon. However, saving does not only consume energy, but may also cause information overload and prevent people from staying focused and working effectively. We present and systematically examine an explanatory AI system (Dare2Del), which supports individuals to delete irrelevant digital objects. To give recommendations for the optimization of related human-computer interactions, we vary different design features (explanations, familiarity, verifiability) within and across three experiments (N(1) = 61, N(2) = 33, N(3)= 73). Moreover, building on the concept of distributed cognition, we check possible cross-connections between external (digital) and internal (human) memory. Specifically, we examine whether deleting external files also contributes to human forgetting of the related mental representations. Multilevel modeling results show the importance of presenting explanations for the acceptance of deleting suggestions in all three experiments, but also point to the need of their verifiability to generate trust in the system. However, we did not find clear evidence that deleting computer files contributes to human forgetting of the related memories. Based on our findings, we provide basic recommendations for the design of AI systems that can help to reduce the burden on people and the digital environment, and suggest directions for future research. Frontiers Media S.A. 2022-11-23 /pmc/articles/PMC9727201/ /pubmed/36504690 http://dx.doi.org/10.3389/frai.2022.919534 Text en Copyright © 2022 Göbel, Niessen, Seufert and Schmid. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Göbel, Kyra Niessen, Cornelia Seufert, Sebastian Schmid, Ute Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations |
title | Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations |
title_full | Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations |
title_fullStr | Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations |
title_full_unstemmed | Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations |
title_short | Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations |
title_sort | explanatory machine learning for justified trust in human-ai collaboration: experiments on file deletion recommendations |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727201/ https://www.ncbi.nlm.nih.gov/pubmed/36504690 http://dx.doi.org/10.3389/frai.2022.919534 |
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