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Evidence record 19121 · automatically gathered

Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements

arXiv:2608.13444v1 Announce Type: new Abstract: Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegitimate, thereby contributing to harm; and being inva

Record details

Published: 14 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness
Retrieved: 14 August 2026

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Same publisher byline · Shared topic: Bias & fairness

Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements

arXiv · 13 August 2026

Shared topic: Bias & fairness

The Illusion of Improvement: Reject Inference Strategies in Credit Scoring

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Shared topic: Bias & fairness

Applied and Filtered: An End-to-End Algorithmic Fairness Audit of A Public Employment Agency

arXiv cs.CY · 14 August 2026

Shared topic: Bias & fairness

From Fair Representation to Just Recognition in Generative AI

arXiv cs.CY · 14 August 2026

Shared topic: Bias & fairness

Reassessing demographic bias in face attribute classification: a statistically grounded multi-model evaluation on FairFace and UTKFace

Frontiers in Artificial Intelligence · 14 August 2026

Shared topic: Bias & fairness

Language Models as a Challenge for Business Ethics – A Partially Open-Source Approach

Science and Engineering Ethics · 14 August 2026

How to cite this record

ethics.ai (14 August 2026), “Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements,” evidence record 19121, https://ethics.ai/record/19121 (originally published by arXiv cs.CY).

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