# Evaluating Proposed Fairness Models for Face Recognition Algorithms arXiv: https://arxiv.org/abs/2203.05051 ยท HTML: https://mlsanity.com/paper/2203.05051/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 13 of its 81 references that are in the map. - First posted on arXiv 9 Mar 2022. ## Builds on (most cited first, 13 of 13) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016) - [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016) - [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017) - [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019) - [Minimax Pareto Fairness: A Multi Objective Perspective](https://mlsanity.com/paper/2011.01821/) (2020) - [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://mlsanity.com/paper/1811.10104/) (2018) - [Demographic Bias in Biometrics: A Survey on an Emerging Challenge](https://mlsanity.com/paper/2003.02488/) (2020) - [Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?](https://mlsanity.com/paper/1912.07398/) (2019) - [Fairness in Biometrics: a figure of merit to assess biometric verification systems](https://mlsanity.com/paper/2011.02395/) (2020) - [The Fairness-Accuracy Pareto Front](https://mlsanity.com/paper/2008.10797/) (2020) - [How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees](https://mlsanity.com/paper/2006.12399/) (2020) - [Quantifying the Extent to Which Race and Gender Features Determine Identity in Commercial Face Recognition Algorithms](https://mlsanity.com/paper/2010.07979/) (2020) ## Built on by (most cited first, 18 of 18) - [Synthetic Data for the Mitigation of Demographic Biases in Face Recognition](https://mlsanity.com/paper/2402.01472/) (2024) - [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026) - [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025) - [Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation](https://mlsanity.com/paper/2311.06000/) (2023) - [Score Normalization for Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2407.14087/) (2024) - [Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition](https://mlsanity.com/paper/2408.10175/) (2024) - [KVC-onGoing: Keystroke Verification Challenge](https://mlsanity.com/paper/2412.20530/) (2024) - [Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometrics](https://mlsanity.com/paper/2409.01928/) (2024) - [On the Illusion of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes](https://mlsanity.com/paper/2501.12020/) (2025) - [Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics](https://mlsanity.com/paper/2506.10564/) (2025) - [Sum of Group Error Differences: A Critical Examination of Bias Evaluation in Biometric Verification and a Dual-Metric Measure](https://mlsanity.com/paper/2404.15385/) (2024) - [Fairness measures for biometric quality assessment](https://mlsanity.com/paper/2408.11392/) (2024) - [Accuracy and Fairness of Facial Recognition Technology in Low-Quality Police Images: An Experiment With Synthetic Faces](https://mlsanity.com/paper/2505.14320/) (2025) - [DemoBias: An Empirical Study to Trace Demographic Biases in Vision Foundation Models](https://mlsanity.com/paper/2508.19298/) (2025) - [Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification](https://mlsanity.com/paper/2603.25613/) (2026) - [Generalizability and Application of the Skin Reflectance Estimate Based on Dichromatic Separation (SREDS)](https://mlsanity.com/paper/2309.01235/) (2023) - [A Responsible Face Recognition Approach for Small and Mid-Scale Systems Through Personalized Neural Networks](https://mlsanity.com/paper/2505.19920/) (2025) - [Variational Latent Entropy Estimation Disentanglement: Controlled Attribute Leakage for Face Recognition](https://mlsanity.com/paper/2604.11250/) (2026) ## Abstract (arXiv) The development of face recognition algorithms by academic and commercial organizations is growing rapidly due to the onset of deep learning and the widespread availability of training data. Though tests of face recognition algorithm performance indicate yearly performance gains, error rates for many of these systems differ based on the demographic composition of the test set. These "demographic differentials" in algorithm performance can contribute to unequal or unfair outcomes for certain groups of people, raising concerns with increased worldwide adoption of face recognition systems. Consequently, regulatory bodies in both the United States and Europe have proposed new rules requiring audits of biometric systems for "discriminatory impacts" (European Union Artificial Intelligence Act) and "fairness" (U.S. Federal Trade Commission). However, no standard for measuring fairness in biometric systems yet exists. This paper characterizes two proposed measures of face recognition algorithm fairness (fairness measures) from scientists in the U.S. and Europe. We find that both proposed methods are challenging to interpret when applied to disaggregated face recognition error rates as they are commonly experienced in practice. To address this, we propose a set of interpretability criteria, termed the Functional Fairness Measure Criteria (FFMC), that outlines a set of properties desirable in a face recognition algorithm fairness measure. We further develop a new fairness measure, the Gini Aggregation Rate for Biometric Equitability (GARBE), and show how, in conjunction with the Pareto optimization, this measure can be used to select among alternative algorithms based on the accuracy/fairness trade-space. Finally, we have open-sourced our dataset of machine-readable, demographically disaggregated error rates. We believe this is currently the largest open-source dataset of its kind.