What is it about?
This research explores racial bias in face recognition technology, a field widely used in areas like security, employment, and public surveillance. While face recognition systems can achieve high accuracy, they often perform unevenly across racial groups, raising ethical and fairness concerns. This study examines where and why these biases occur and evaluates potential solutions.
Featured Image
Photo by Doug Swinson on Unsplash
Why is it important?
This study tackles racial bias within face recognition, a technology increasingly shaping security, hiring, and public services. By dissecting biases across every stage—from data collection to decision-making—it exposes flaws that perpetuate unfair outcomes, especially for marginalized groups. Its comprehensive analysis and actionable strategies empower researchers, developers, and policymakers to create fairer, more inclusive systems. This work sets a new standard for ethical AI, driving equity, trust, and global impact.
Perspectives
This publication provides a clear and accessible breakdown of how seemingly small algorithmic decisions in face recognition can significantly influence outcomes, leading to bias. It unpacks the complexities of this widely used technology, highlighting its strategic role as a prominent task closely connected to many steps in any AI-driven technology, from security to decision-making processes. Moreover, it exemplifies how technically-focused researchers can critically engage with the societal outcomes of their work, offering a thoughtful model for ethical AI development.
Seyma Yucer
Durham University
Read the Original
This page is a summary of: Racial Bias within Face Recognition: A Survey, ACM Computing Surveys, November 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3705295.
You can read the full text:
Contributors
The following have contributed to this page
