Demographic Bias in Biometrics: A Survey on an Emerging Challenge (2003.02488v2)

Abstract: Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g. access control) and non-cooperative (e.g. surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioural characteristics of human beings, which enable for individuals to be reliably recognised using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labelled as "racist" or "biased" by the media, non-governmental organisations, and researchers alike. The main contributions of this article are: (1) an overview of the topic of algorithmic bias in the context of biometrics, (2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation, (3) a discussion of the pertinent technical and social matters, and (4) an outline of the remaining challenges and future work items, both from technological and social points of view.

Analyzing Demographic Bias in Biometric Systems

Biometric systems have become a crucial component of various personal, commercial, and governmental applications, utilized for identity verification and surveillance tasks. Despite the technological advancements and efficiency of these systems, concerns about demographic bias have garnered attention, especially considering the impact of such biases on fairness, ethics, and social implications. The paper "Demographic Bias in Biometrics: A Survey on an Emerging Challenge" by P. Drozdowski et al. presents a comprehensive analysis of demographic bias within biometric systems, evaluates existing methodologies for bias estimation and mitigation, and discusses future directions for research and development in this domain.

The paper identifies that biometric systems, regardless of their application in cooperative (e.g., access control) or non-cooperative (e.g., surveillance) settings, have reported biases that manifest in differential performance and differential outcomes. These biases often correlate with attributes such as gender, race, and age, thereby affecting the operational accuracy across different demographic groups.

Key Contributions

1. Survey on Biometric Bias Estimation: The paper systematically reviews numerous studies focused on bias estimation across various biometric modalities, including facial and fingerprint recognition systems. Notably, facial biometric studies dominate the literature, pointing to a consistent trend of reduced accuracy for females and individuals with darker skin tones. Moreover, the age factor, especially with very young or elderly subjects, continues to challenge existing systems.
2. Mitigation Strategies: Various strategies for mitigating demographic bias are explored in the paper. These include ensuring balanced training datasets, employing synthetic datasets, and developing algorithms for dynamic threshold selection or matcher algorithms based on demographic attributes. Techniques such as adversarial learning and attribute-suppression methodologies are suggested to help neutralize disparities in error metrics.
3. Technical and Social Discussion: The paper emphasizes the technical challenges in isolating demographic factors due to intertwined covariates in biometric datasets. It highlights the lack of a standard and consistent measure of "fairness" in biometric operations, which complicates the task of defining and assessing bias. Further, the potential social impacts of biased biometric systems range from minor user inconveniences to significant legal and ethical repercussions.

Future Directions

The authors outline several avenues for future work, emphasizing the need for:

• Standardized Evaluations: Developing rigorous, standardized evaluation metrics to facilitate uniform bias characterization and mitigate inconsistencies in paper outcomes.
• Comprehensive Datasets: Establishing large-scale and publicly accessible datasets explicitly curated for demographic bias studies to improve reproducibility and enhance algorithm training.
• Interdisciplinary Frameworks: Encouraging collaborative efforts across data science, law, and humanities to navigate the ethical, legal, and societal implications of biased biometric systems.

Implications

Practically, the research underscores the necessity for developers and policymakers to prospectively incorporate fairness auditing mechanisms and ensure transparency within biometric systems. Theoretically, the paper positions itself as a foundational text for scholars in the domain, expanding the discourse on fairness and accountability in biometric systems. The cumulative insights present a compelling call to action for industry stakeholders and researchers to enhance system design for equitable utility across diverse demographic landscapes.

Through this survey, Drozdowski et al. not only advance the understanding of demographic bias in biometrics but also advocate for a strategic shift towards inclusive and fair algorithmic practices. The exploration of this pivotal challenge in biometrics stands to influence the design and deployment of future biometric technologies, aligning them with broader ethical standards and societal expectations.