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

• URL: http://arxiv.org/abs/2608.13444v1
• Date: Thu, 13 Aug 2026 16:30:47 GMT
• Title: Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements
• Abstract summary: We show how gender prediction can be illegitimate yet still yield valid disparity measurements.<n>While gender imputation can produce valid measurements for the former, it is illegitimate and harmful for the latter.<n>We conclude by recommending the development of more inclusive methods to address all kinds of sexism.
• Score: 6.129917268639289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 invalid, thereby producing unusable measurements. Our analysis translates arguments against gender prediction into these terms of legitimacy and validity and shows how gender imputation applied for fairness purposes can be illegitimate yet still yield valid disparity measurements. We clarify this bind by drawing upon transfeminist literature to distinguish sexism that targets women and femininity from sexism that targets transgender and nonbinary people. While gender imputation can produce valid measurements for the former, it is illegitimate and harmful for the latter. We argue that practitioners should deploy gender imputation only when it would achieve anti-discrimination benefits that cannot be achieved through other reasonable means, while harms are minimized to the extent possible. We examine this tension in three case studies: auditing gender bias in generative image models, measuring gender disparities in film, and imputing gender from personal names. By disentangling legitimacy from validity, and differentiating these two forms of sexism, we show how debates over gender prediction have conflated distinct concerns, obscuring both the settings in which gender imputation can support fairness efforts and the harms towards transgender and nonbinary people that it fundamentally cannot capture. We conclude by recommending the development of more inclusive methods that address all kinds of sexism.

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