# Understanding bias in facial recognition technologies

arXiv: https://arxiv.org/abs/2010.07023 · HTML: https://mlsanity.com/paper/2010.07023/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition](https://mlsanity.com/paper/2210.09943/) (2022)
- [Learning from data with structured missingness](https://mlsanity.com/paper/2304.01429/) (2023)
- [Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things](https://mlsanity.com/paper/2301.03561/) (2023)
- [Towards Fair Face Verification: An In-depth Analysis of Demographic Biases](https://mlsanity.com/paper/2307.10011/) (2023)
- [Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions](https://mlsanity.com/paper/2303.16310/) (2023)
- [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025)
- [Fairness-Aware Meta-Learning via Nash Bargaining](https://mlsanity.com/paper/2406.07029/) (2024)
- [Zero-shot racially balanced dataset generation using an existing biased StyleGAN2](https://mlsanity.com/paper/2305.07710/) (2023)
- [Exploring Vision Language Models for Facial Attribute Recognition: Emotion, Race, Gender, and Age](https://mlsanity.com/paper/2410.24148/) (2024)
- [Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access](https://mlsanity.com/paper/2302.01385/) (2023)
- [Test-Time Augmentation for Pose-invariant Face Recognition](https://mlsanity.com/paper/2505.09256/) (2025)
- [Ethnicity and Biometric Uniqueness: Iris Pattern Individuality in a West African Database](https://mlsanity.com/paper/2309.06521/) (2023)
- [3D2M Dataset: A 3-Dimension diverse Mesh Dataset](https://mlsanity.com/paper/2410.07415/) (2024)
- [Achieving Fair PCA Using Joint Eigenvalue Decomposition](https://mlsanity.com/paper/2502.16933/) (2025)
- [Foundation Artificial Intelligence Models for Health Recognition Using Face Photographs (FAHR-Face)](https://mlsanity.com/paper/2506.14909/) (2025)

## Abstract (arXiv)

Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems. Opponents argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threatens to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. They also caution that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation. Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring. In this explainer, I focus on one central aspect of this debate: the role that dynamics of bias and discrimination play in the development and deployment of FDRTs. I examine how historical patterns of discrimination have made inroads into the design and implementation of FDRTs from their very earliest moments. And, I explain the ways in which the use of biased FDRTs can lead distributional and recognitional injustices. The explainer concludes with an exploration of broader ethical questions around the potential proliferation of pervasive face-based surveillance infrastructures and makes some recommendations for cultivating more responsible approaches to the development and governance of these technologies.