Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior?
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915428192747520 |
|---|---|
| author | Bohlen, Lasse Kruschel, Sven Rosenberger, Julian Zschech, Patrick Kraus, Mathias |
| author_facet | Bohlen, Lasse Kruschel, Sven Rosenberger, Julian Zschech, Patrick Kraus, Mathias |
| contents | Previous work has shown that allowing users to adjust a machine learning (ML) model's predictions can reduce aversion to imperfect algorithmic decisions. However, these results were obtained in situations where users had no information about the model's reasoning. Thus, it remains unclear whether interpretable ML models could further reduce algorithm aversion or even render adjustability obsolete. In this paper, we conceptually replicate a well-known study that examines the effect of adjustable predictions on algorithm aversion and extend it by introducing an interpretable ML model that visually reveals its decision logic. Through a pre-registered user study with 280 participants, we investigate how transparency interacts with adjustability in reducing aversion to algorithmic decision-making. Our results replicate the adjustability effect, showing that allowing users to modify algorithmic predictions mitigates aversion. Transparency's impact appears smaller than expected and was not significant for our sample. Furthermore, the effects of transparency and adjustability appear to be more independent than expected. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior? Bohlen, Lasse Kruschel, Sven Rosenberger, Julian Zschech, Patrick Kraus, Mathias Machine Learning Previous work has shown that allowing users to adjust a machine learning (ML) model's predictions can reduce aversion to imperfect algorithmic decisions. However, these results were obtained in situations where users had no information about the model's reasoning. Thus, it remains unclear whether interpretable ML models could further reduce algorithm aversion or even render adjustability obsolete. In this paper, we conceptually replicate a well-known study that examines the effect of adjustable predictions on algorithm aversion and extend it by introducing an interpretable ML model that visually reveals its decision logic. Through a pre-registered user study with 280 participants, we investigate how transparency interacts with adjustability in reducing aversion to algorithmic decision-making. Our results replicate the adjustability effect, showing that allowing users to modify algorithmic predictions mitigates aversion. Transparency's impact appears smaller than expected and was not significant for our sample. Furthermore, the effects of transparency and adjustability appear to be more independent than expected. |
| title | Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior? |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.03168 |