# Does Calibration Affect Human Actions?

arXiv: https://arxiv.org/abs/2508.18317 · HTML: https://mlsanity.com/paper/2508.18317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [A Simple Baseline for Bayesian Uncertainty in Deep Learning](https://mlsanity.com/paper/1902.02476/) (2019)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification](https://mlsanity.com/paper/1805.10915/) (2018)
- [Uncalibrated Models Can Improve Human-AI Collaboration](https://mlsanity.com/paper/2202.05983/) (2022)

## Built on by (most cited first, 1 of 1)

- [Aligning Probabilistic Beliefs under Informative Missingness: LLM Steerability in Clinical Reasoning](https://mlsanity.com/paper/2512.00479/) (2025)

## Abstract (arXiv)

Calibration has been proposed as a way to enhance the reliability and adoption of machine learning classifiers. We study a particular aspect of this proposal: how does calibrating a classification model affect the decisions made by non-expert humans consuming the model's predictions? We perform a Human-Computer-Interaction (HCI) experiment to ascertain the effect of calibration on (i) trust in the model, and (ii) the correlation between decisions and predictions. We also propose further corrections to the reported calibrated scores based on Kahneman and Tversky's prospect theory from behavioral economics, and study the effect of these corrections on trust and decision-making. We find that calibration is not sufficient on its own; the prospect theory correction is crucial for increasing the correlation between human decisions and the model's predictions. While this increased correlation suggests higher trust in the model, responses to ``Do you trust the model more?" are unaffected by the method used.