---
title: "Algorithm aversion in the era of fluent machines"
description: "Trust in AI appears on 76 recent papers, algorithm aversion on 10 and algorithm appreciation on 3, across the AI and management journals, with 28 papers in the last quarter against about 25 per quarter before. People once rejected algorithmic advice after seeing it err. Language models that explain themselves fluently may get too much trust instead. Which way does it go, and for whom?"
url: https://scholar4ai.com/ideas/algorithm-aversion-in-the-llm-era
markdown_url: https://scholar4ai.com/ideas/algorithm-aversion-in-the-llm-era.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Trust in AI appears on 76 recent papers, algorithm aversion on 10 and algorithm appreciation on 3, across the AI and management journals, with 28 papers in the last quarter against about 25 per quarter before."
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "ai", "decision-making"]
keywords: ["algorithm aversion", "trust in AI", "algorithm appreciation"]
---

# Algorithm aversion in the era of fluent machines

Trust in AI appears on 76 recent papers, algorithm aversion on 10 and algorithm appreciation on 3, across the AI and management journals, with 28 papers in the last quarter against about 25 per quarter before. People once rejected algorithmic advice after seeing it err. Language models that explain themselves fluently may get too much trust instead. Which way does it go, and for whom?

## What prompted this

Since October 2025, 106 papers carry a trust-in-AI, algorithm-aversion, algorithm-appreciation or AI-advice keyword (52 in AI journals, 37 in management): "trust in AI" on 76, "algorithm aversion" on 10, "public trust in AI" on 4, "algorithm appreciation" on 3. The quarter to September 2026 had 28 such papers against about 25 per quarter earlier.

## Questions

- In the same forecasting task, do people follow a language model's advice more than a statistical model's, and does the difference survive after both make the same error?
- Does a fluent explanation increase reliance even when it is wrong, and can calibration cues (stated uncertainty) correct that?
- Do professionals (doctors, loan officers, analysts) show aversion or over-reliance in their own domain, compared with laypeople?

## Data that could help

Incentivized experiments with advice from different sources, and field data from professional settings where model recommendations and human decisions are both logged.

## Next step

Run an experiment in which participants receive identical forecasts framed as coming from a statistical model, a language model with explanations, or a human expert, and measure reliance after errors.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 23 – Oct 26* | Papers, last 36 months |
|---|---:|---:|---|---:|
| algorithm aversion | 1 | #2881 | 0, 1, 1, 0, 0, 2, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 1, 3, 0, 1, 0, 1, 1 | 22 |
| trust in AI | 5 | #393 | 2, 3, 1, 2, 3, 1, 3, 3, 3, 4, 0, 2, 6, 5, 3, 2, 2, 8, 6, 4, 3, 10, 2, 7, 8, 7, 4, 9, 4, 12, 8, 7, 3, 7, 11, 4, 4 | 169 |
| algorithm appreciation | 0 | – | 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0 | 5 |

## Cite this page

Scholar4AI. (2026). Algorithm aversion in the era of fluent machines. Journal Research Rankings. Retrieved October 23, 2026, from https://scholar4ai.com/ideas/algorithm-aversion-in-the-llm-era

```bibtex
@misc{scholar4ai-ideas-algorithm-aversion-in-the-llm-era,
  author = {{Scholar4AI}},
  title = {Algorithm aversion in the era of fluent machines},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/ideas/algorithm-aversion-in-the-llm-era}},
  urldate = {2026-10-23},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).