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Evidence record 19164 · automatically gathered

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

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 invalid, thereby producing unusable measurements. Ou

Record details

Published: 13 August 2026
Source: arXiv
Category: Research
Topics: Bias & fairness
Retrieved: 14 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Bias & fairness

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

arXiv cs.CY · 14 August 2026

Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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Frontiers in Artificial Intelligence · 14 August 2026

Shared topic: Bias & fairness

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How to cite this record

ethics.ai (13 August 2026), “Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements,” evidence record 19164, https://ethics.ai/record/19164 (originally published by arXiv).

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