TIM-6074

Agarwal, A., Kumar, M., & Nene, M. J. (2025). Enhancements for developing a comprehensive AI fairness assessment standard. IEEE Conference Publication.
This source discusses the need for enhanced AI fairness assessment standards to keep pace with evolving AI technologies, particularly for unstructured data and generative AI, to ensure responsible deployment in critical sectors.

Alvarez, J. M., Bringas Colmenarejo, A., Elobaid, A., Fabbrizzi, S., Fahimi, M., Ferrara, A., Ghodsi, S., Mougan, C., Papageorgiou, I., Reyero, P., Russo, M., Scott, K. M., State, L., Zhao, X., & Ruggieri, S. (2024). Policy advice and best practices on bias and fairness in AI. Ethics Inf Technol 26, 3.
This paper surveys the state-of-the-art in AI fairness methods and policies, discusses sources of bias, critiques the overreliance on technical metrics, highlights the tension between fairness and other AI qualities, advocates for multidisciplinary approaches and stakeholder involvement, and addresses the implications of societal reliance on AI.

Bura, C., Myakala, P. K., & Jonnalagadda, A. K. (2025). Ethical prompt engineering: Addressing bias, transparency, and fairness. International Journal of Research and Analytical Reviews, 12.
https://www.researchgate.net/publication/389819761_Ethical_Prompt_Engineering_Addressing_BiasTransparency_and_Fairness
This source examines the role of prompt engineering in addressing bias, transparency, and fairness in AI-generated content from Large Language Models, reviewing bias detection and mitigation strategies, and proposing frameworks for responsible prompt design.

González-Sendino, R., Serrano, E., Bajo, J., & Novais, P. (2024). A review of bias and fairness in artificial intelligence. International Journal of Interactive Multimedia and Artificial Intelligence, 9. 5-17. https://www.ijimai.org/journal/bibcite/reference/3390
This review provides a comprehensive analysis of bias and fairness in AI, categorizing biases by development phase, reviewing fairness metrics, outlining mitigation procedures, and highlighting challenges in quantifying fairness and the need for fairness-by-design standards.

Paik, S., Novozhilova, E., Mays, K. K., & Katz, J. E. (2025). Who benefits from AI? Examining different demographics’ fairness perceptions across personal, work, and public life. Discover Artificial Intelligence. 5:39.
https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1007%2Fs44163-025-00235-7%3Fpdf%3Dopenurl
This study investigates public perceptions of AI fairness based on perceived harms and benefits across personal, work, and public contexts, highlighting demographic differences in perception and discussing the concept of the "automation divide".

Pant, A., Hoda, R., Tantithamthavorn, C., & Turhan, B. (2025). Navigating fairness: Practitioners’ understanding, challenges, and strategies in AI/ML development. Empirical Software Engineering.
https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1007%2Fs10664-025-10650-0%3Fpdf%3Dopenurl
This paper explores AI practitioners' perspectives on developing fair AI/ML systems, revealing their understanding of fairness, the challenges they face (process, resource, team-related), the perceived consequences of unfair systems, and the strategies they employ for fairness.

Swoboda, T., & Lauwaert, L. (2025). Can artificial intelligence embody moral values? AI and Ethics.
https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1007%2Fs43681-025-00662-3%3Fpdf%3Dopenurl
This source challenges the neutrality thesis of technology, arguing that AI systems can embody moral values like fairness, thus being non-neutral in a desirable way, and examines computational models for integrating moral values.

Ueda, M., Yamaguchi, S., & Nakayama, K. (2024). Fairness of artificial intelligence in healthcare: Review and recommendations. Japanese Journal of Radiology, 42. 3–15.
https://research.ebsco.com/linkprocessor/plink?id=ae217d76-dfe5-3c11-8ae8-41ce84dbe061
This review focuses on AI fairness in healthcare, discussing biases and their causes, potential concerns like misdiagnosis, suggesting mitigation strategies such as using diverse data and validating algorithms, and highlighting ethical/legal issues and the need for stakeholder collaboration.

Zhang, W. (2024). AI fairness in practice: Paradigm, challenges, and prospects. AI Magazine
https://doi.org/10.1002/aaai.12189
This paper reviews recent advances in AI fairness aimed at practical deployment, highlighting limitations in traditional research paradigms and the challenges posed by real-world data complexities, particularly noting the limited focus on intersectionality.