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.