# AI Recommendations and Non-instrumental Image Concerns

arXiv: https://arxiv.org/abs/2504.19047 · HTML: https://mlsanity.com/paper/2504.19047/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 26 Apr 2025.

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

- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Algorithmic Assistance with Recommendation-Dependent Preferences](https://mlsanity.com/paper/2208.07626/) (2022)
- [On the Fairness of Machine-Assisted Human Decisions](https://mlsanity.com/paper/2110.15310/) (2021)
- [AI Oversight and Human Mistakes: Evidence from Centre Court](https://mlsanity.com/paper/2401.16754/) (2024)
- [Rethinking Algorithmic Fairness for Human-AI Collaboration](https://mlsanity.com/paper/2310.03647/) (2023)
- [Reputational Algorithm Aversion](https://mlsanity.com/paper/2402.15418/) (2024)

## Abstract (arXiv)

There is growing enthusiasm about the potential for humans and AI to collaborate by leveraging their respective strengths. Yet in practice, this promise often falls short. This paper uses an online experiment to identify non-instrumental image concerns as a key reason individuals underutilize AI recommendations. I show that concerns about how one is perceived, even when those perceptions carry no monetary consequences, lead participants to disregard AI advice and reduce task performance.