Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds.
Algorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectiv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0247084 |
| _version_ | 1818902622241816576 |
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| author | Achiel Fenneman Joern Sickmann Thomas Pitz Alan G Sanfey |
| author_facet | Achiel Fenneman Joern Sickmann Thomas Pitz Alan G Sanfey |
| author_sort | Achiel Fenneman |
| collection | DOAJ |
| description | Algorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectively avoid algorithmic systems. The mechanisms that give rise to these individual differences are currently poorly understood. Previous studies have suggested two possible effects that may underlie this variability: users may differ in their predictions of the efficacy of algorithmic systems, and/or in the relative thresholds they hold to place trust in these systems. Based on a novel judgment task with a large number of within-subject repetitions, here we report evidence that both mechanisms exert an effect on experimental participant's degree of algorithm adherence, but, importantly, that these two mechanisms are independent from each-other. Furthermore, participants are more likely to place their trust in an algorithmically managed fund if their first exposure to the task was with an algorithmic manager. These findings open the door for future research into the mechanisms driving individual differences in algorithm adherence, and allow for novel interventions to increase adherence to algorithms. |
| first_indexed | 2024-12-19T20:38:34Z |
| format | Article |
| id | doaj.art-18eeecf8ea9645039a78c596015275d3 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T20:38:34Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-18eeecf8ea9645039a78c596015275d32022-12-21T20:06:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01162e024708410.1371/journal.pone.0247084Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds.Achiel FennemanJoern SickmannThomas PitzAlan G SanfeyAlgorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectively avoid algorithmic systems. The mechanisms that give rise to these individual differences are currently poorly understood. Previous studies have suggested two possible effects that may underlie this variability: users may differ in their predictions of the efficacy of algorithmic systems, and/or in the relative thresholds they hold to place trust in these systems. Based on a novel judgment task with a large number of within-subject repetitions, here we report evidence that both mechanisms exert an effect on experimental participant's degree of algorithm adherence, but, importantly, that these two mechanisms are independent from each-other. Furthermore, participants are more likely to place their trust in an algorithmically managed fund if their first exposure to the task was with an algorithmic manager. These findings open the door for future research into the mechanisms driving individual differences in algorithm adherence, and allow for novel interventions to increase adherence to algorithms.https://doi.org/10.1371/journal.pone.0247084 |
| spellingShingle | Achiel Fenneman Joern Sickmann Thomas Pitz Alan G Sanfey Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds. PLoS ONE |
| title | Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds. |
| title_full | Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds. |
| title_fullStr | Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds. |
| title_full_unstemmed | Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds. |
| title_short | Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds. |
| title_sort | two distinct and separable processes underlie individual differences in algorithm adherence differences in predictions and differences in trust thresholds |
| url | https://doi.org/10.1371/journal.pone.0247084 |
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