# Does calibration mean what they say it means; or, the reference class problem rises again

arXiv: https://arxiv.org/abs/2412.16769 · HTML: https://mlsanity.com/paper/2412.16769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 21 Dec 2024.

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

- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Reconciling Individual Probability Forecasts](https://mlsanity.com/paper/2209.01687/) (2022)
- [Fairness in machine learning: against false positive rate equality as a measure of fairness](https://mlsanity.com/paper/2007.02890/) (2020)
- [On the Richness of Calibration](https://mlsanity.com/paper/2302.04118/) (2023)
- [Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory](https://mlsanity.com/paper/2205.05512/) (2022)

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

- [Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI](https://mlsanity.com/paper/2508.07872/) (2025)

## Abstract (arXiv)

Discussions of statistical criteria for fairness commonly convey the normative significance of calibration within groups by invoking what risk scores "mean." On the Same Meaning picture, group-calibrated scores "mean the same thing" (on average) across individuals from different groups and accordingly, guard against disparate treatment of individuals based on group membership. My contention is that calibration guarantees no such thing. Since concrete actual people belong to many groups, calibration cannot ensure the kind of consistent score interpretation that the Same Meaning picture implies matters for fairness, unless calibration is met within every group to which an individual belongs. Alas only perfect predictors may meet this bar. The Same Meaning picture thus commits a reference class fallacy by inferring from calibration within some group to the "meaning" or evidential value of an individual's score, because they are a member of that group. The reference class answer it presumes does not only lack justification; it is very likely wrong. I then show that the reference class problem besets not just calibration but other group statistical criteria that claim a close connection to fairness. Reflecting on the origins of this oversight opens a wider lens onto the predominant methodology in algorithmic fairness based on stylized cases.