On the choice of using raw or demographically-corrected scores

2026-06-30

AI Summary

This study investigates the impact of using raw scores versus demographically adjusted scores on classification accuracy and decision fairness in cognitive screening. Through theoretical analysis and empirical validation on the OASIS-3 dataset, it rigorously derives, for the first time, sufficient conditions under which raw scores outperform adjusted scores. The findings demonstrate that common adjustment methods—such as z-score normalization—not only can degrade classification performance under certain conditions but also do not necessarily enhance fairness, thereby challenging the widely held assumption that statistical correction inherently promotes equitable outcomes. This work provides a principled theoretical foundation and practical guidance for score selection in cognitive assessment protocols.

Abstract

Demographic corrections are routinely performed in many disciplines, including psychology. Yet, there are ongoing debates about whether these corrections are appropriate and improve classification accuracy. Here, we focus on cognitive screening tests, and show that common demographic corrections, like the z-score standardization, can be detrimental for classification in some settings. Formally, we present sufficient conditions ensuring that raw scores outperform the demographically-corrected ones, and give a substantive interpretation of this result. We also investigate the claim that using demographically-corrected scores results in more fair decisions compared to using raw scores. We apply our results to the Mini-Mental State Examination in the OASIS-3 dataset.

Problem

• demographic correction
• cognitive screening
• classification accuracy
• fairness
• raw scores

Innovation

• demographic correction
• raw scores
• classification accuracy
• cognitive screening
• fairness