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The extent of algorithm aversion in decision-making situations with varying gravity
Algorithms already carry out many tasks more reliably than human experts. Nevertheless, some subjects have an aversion towards algorithms. In some decision-making situations an error can have serious consequences, in others not. In the context of a framing experiment, we examine the connection betwe...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942970/ https://www.ncbi.nlm.nih.gov/pubmed/36809526 http://dx.doi.org/10.1371/journal.pone.0278751 |
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author | Filiz, Ibrahim Judek, Jan René Lorenz, Marco Spiwoks, Markus |
author_facet | Filiz, Ibrahim Judek, Jan René Lorenz, Marco Spiwoks, Markus |
author_sort | Filiz, Ibrahim |
collection | PubMed |
description | Algorithms already carry out many tasks more reliably than human experts. Nevertheless, some subjects have an aversion towards algorithms. In some decision-making situations an error can have serious consequences, in others not. In the context of a framing experiment, we examine the connection between the consequences of a decision-making situation and the frequency of algorithm aversion. This shows that the more serious the consequences of a decision are, the more frequently algorithm aversion occurs. Particularly in the case of very important decisions, algorithm aversion thus leads to a reduction of the probability of success. This can be described as the tragedy of algorithm aversion. |
format | Online Article Text |
id | pubmed-9942970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99429702023-02-22 The extent of algorithm aversion in decision-making situations with varying gravity Filiz, Ibrahim Judek, Jan René Lorenz, Marco Spiwoks, Markus PLoS One Research Article Algorithms already carry out many tasks more reliably than human experts. Nevertheless, some subjects have an aversion towards algorithms. In some decision-making situations an error can have serious consequences, in others not. In the context of a framing experiment, we examine the connection between the consequences of a decision-making situation and the frequency of algorithm aversion. This shows that the more serious the consequences of a decision are, the more frequently algorithm aversion occurs. Particularly in the case of very important decisions, algorithm aversion thus leads to a reduction of the probability of success. This can be described as the tragedy of algorithm aversion. Public Library of Science 2023-02-21 /pmc/articles/PMC9942970/ /pubmed/36809526 http://dx.doi.org/10.1371/journal.pone.0278751 Text en © 2023 Filiz et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Filiz, Ibrahim Judek, Jan René Lorenz, Marco Spiwoks, Markus The extent of algorithm aversion in decision-making situations with varying gravity |
title | The extent of algorithm aversion in decision-making situations with varying gravity |
title_full | The extent of algorithm aversion in decision-making situations with varying gravity |
title_fullStr | The extent of algorithm aversion in decision-making situations with varying gravity |
title_full_unstemmed | The extent of algorithm aversion in decision-making situations with varying gravity |
title_short | The extent of algorithm aversion in decision-making situations with varying gravity |
title_sort | extent of algorithm aversion in decision-making situations with varying gravity |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942970/ https://www.ncbi.nlm.nih.gov/pubmed/36809526 http://dx.doi.org/10.1371/journal.pone.0278751 |
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