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

Research differentiates between the 'illegitimacy' and 'invalidity' of algorithmic gender prediction. While direct gender prediction is often deemed illegitimate due to ethical concerns and potential harm, the process of gender 'imputation' used for fairness analysis can still yield valid measurements for studying disparities, even if its underlying methodology is considered ethically problematic. This distinction aims to reconcile seemingly contradictory perspectives within machine learning ethics and fairness research.

Why it matters

This research highlights a fundamental tension in the application of AI and data science concerning sensitive attributes like gender. Organizations must understand the ethical and practical implications of using such data, particularly when balancing data utility for fairness analyses against the potential for harm or ethical compromise in predictive systems. It informs strategy around responsible AI development and deployment, especially in areas touching on societal equity.

Key insights

• Machine learning ethics and critical Human-Computer Interaction scholars frequently argue that algorithmic prediction of gender is ethically wrong and potentially harmful.
• Conversely, some researchers utilize predicted gender labels to analyze gender disparities and develop algorithmic fairness techniques.
• The analysis introduces a critical distinction between algorithmic gender prediction being 'illegitimate' (contributing to harm) and 'invalid' (producing unusable measurements).
• It is posited that gender imputation, when applied for fairness purposes, can be considered illegitimate in its nature but still generate valid measurements for understanding disparities.
• The research clarifies the tension between the ethical concerns surrounding gender prediction and its practical application in fairness-oriented studies.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.13444