RACIAL AND CULTURAL IDENTIFICATION CHALLENGES IN FACIAL RECOGNITION: A CENTRAL ASIAN PERSPECTIVE

Nodira Yunusova

Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Faculty Of Multimedia Technologies Department of Data science Master's degree student

Keywords: facial recognition, central asia, algorithmic bias, cultural identity, biometric data, misidentification, ethnic diversity, ai fairness.

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Abstract

This paper examines the racial and cultural identification challenges of facial recognition technologies from a Central Asian perspective. Despite rapid adoption in security, governance, and digital services, most systems are trained on datasets dominated by Western and East Asian populations, leading to misidentification of Central Asian ethnic groups. Diverse phenotypes, traditional attire, and inconsistent biometric standards further reduce accuracy. Weak legal frameworks and limited public awareness heighten risks of bias, privacy violations, and misuse. The paper emphasizes the need for inclusive datasets, culturally informed system design, and strong regulatory protections to ensure fairness, representation, and ethical implementation in the region.

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References

1. Rakhimov, Sh. (2021). Artificial Intelligence and Identification Technologies in Central Asia. Scientific Collection of Samarkand State University.

2. Turdieva, M., & Karimova, D. (2019). Legal Foundations of Biometric Data Protection. Journal of Tashkent State University of Law.

3. Yusupov, B. (2018). Anthropology and Facial Morphology of Uzbek Peoples. Tashkent: Science and Technology.

4. Mukhamedov, S., & Tursunova, G. (2022). Digital Governance and Identity Verification in Central Asia. Bulletin of Social Sciences, 4(2).

5. Klare, B., Burton, A., Klontz, J., Bruegge, R., & Jain, A. (2012).

Face Recognition Performance: Role of Demographic Information.

IEEE Transactions on Information Forensics and Security.

6. Garvie, C., Bedoya, A., & Frankle, J. (2016).

The Perpetual Line-Up: Unregulated Police Face Recognition in America.

Georgetown Law Center on Privacy & Technology.

7. Phillips, P., Jiang, F., Narvekar, A., Ayyad, J., & O’Toole, A. (2011).

An Other-Race Effect for Face Recognition Algorithms.

ACM Transactions on Applied Perception.

8. Whittaker, M., Crawford, K., Dobbe, R., & Kaziunas, E. (2018).

AI Now Report: Bias, Rights, and Regulation in AI.

AI Now Institute, New York University.