# Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior?

arXiv: https://arxiv.org/abs/2508.03168 · HTML: https://mlsanity.com/paper/2508.03168/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values](https://mlsanity.com/paper/2206.15465/) (2022)
- [Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control](https://mlsanity.com/paper/2303.12896/) (2023)
- [Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models](https://mlsanity.com/paper/2409.14429/) (2024)

## Built on by (most cited first, 2 of 2)

- [Advancing credit mobility through stakeholder-informed AI design and adoption](https://mlsanity.com/paper/2601.05666/) (2026)
- [Beyond Accuracy: How Humans Evaluate Legally Correct but Socially Controversial Legal Advice from Machines](https://mlsanity.com/paper/2607.05680/) (2026)

## Abstract (arXiv)

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.