Cognitive Biases:
Understanding and Designing Fair AI Systems for Software Development
DOI:
https://doi.org/10.60087/jklst.v4.n2.004Keywords:
Cognitive Biases, Fair AI Systems, Algorithmic Bias, Software Development, Bias Mitigation, Fairness in Software Development, Bias Mitigation in AI SystemsAbstract
Artificial Intelligence (AI) systems, while advancing software development, are often susceptible to cognitive biases that lead to unfair outcomes. This study explores the roles of confirmation bias, anchoring bias, and automation bias in influencing AI decision-making. These biases commonly emerge from unrepresentative datasets, algorithmic design flaws, and subjective human decisions. Through a qualitative methodology involving literature review and case analysis, the research identifies the origins and manifestations of cognitive bias in AI, particularly within domains like criminal justice, healthcare, and recruitment. The study proposes several mitigation strategies: incorporating diverse and representative data, adopting fairness-aware algorithm designs, and conducting routine bias audits. Evaluation criteria include each strategy’s effectiveness, feasibility, transparency, and scalability. Findings indicate that while these techniques significantly improve fairness in AI outputs, they also present practical challenges such as reduced model precision and resource constraints. The study emphasizes that eliminating cognitive bias requires not only technical adjustments but also interdisciplinary collaboration and ethical considerations. The findings serve as a guide for developers, stakeholders, and policymakers aiming to design responsible AI systems that uphold transparency, accountability, and social equity across software development environments.
Downloads
References
Bernault, C., Juan, S., Delmas, A., Andre, J. M., Rodier, M., & Chraibi Kaadoud, I. (2023, July). Assessing the impact of cognitive biases in AI project development. In International Conference on Human-Computer Interaction (pp. 401–420). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35891-3_24
Scatiggio, V. (2020). Tackling the issue of Bias in artificial intelligence to design AI-driven fair and inclusive service systems. How human biases are breaching into AI algorithms, with severe impacts on individuals and societies, and what designers can do to face this phenomenon and change for the better. https://hdl.handle.net/10589/186118
Vakali, A., & Tantalaki, N. (2024). Rolling in the deep of cognitive and AI biases. arXiv preprint arXiv:2407.21202. https://doi.org/10.48550/arXiv.2407.21202
Schwartz, R., Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a standard for iden-tifying and managing Bias in artificial intelligence (Vol. 3, p. 00). Gaithersburg, MD: US Department of Commerce, National Institute of Standards and Technology. https://doi.org/10.7717/peerj-cs.1630
Varona, D., & Suárez, J. L. (2022). Discrimination, Bias, fairness, and trustworthy AI. Applied Sciences, 12(12), 5826. (https://creativecommons.org/licenses/by/4.0/
Chen, Y., Clayton, E. W., Novak, L. L., Anders, S., & Malin, B. (2023). Human-centered design to address biases in arti-ficial intelligence. Journal of medical Internet research, 25, e43251. https://doi.org/10.2196/43251
AlMakinah, R., Goodarzi, M., Tok, B., & Canbaz, M. A. (2024). Mapping artificial intelligence bias: a network-based framework for analysis and mitigation. AI and Ethics, 1-20. 1684–1692 (2023). https://doi.org/10.1093/jamia/ocad118
Bellamy, R. K., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., ... & Zhang, Y. (2019). AI Fairness 360: An ex-tensible toolkit for detecting and mitigating algorithmic Bias. IBM Journal of Research and Development, 63(4/5), 4–1. DOI: 10.1147/JRD.2019.2942287
Fuad, N. R. Cognitive Bias and AI Technology: The Human Element in Automated Recruitment and Selection. 6. https://doi.org/10.1038/d41586-018-05707-8
Tejani, A. S., Ng, Y. S., Xi, Y., & Rayan, J. C. (2024). Understanding and mitigating bias in imaging artificial intelli-gence. Radiographics, 44(5), e230067. https://doi.org/10.1148/rg.230067
Kordzadeh, N., & Ghasemaghaei, M. (2022). Algorithmic Bias: review, synthesis, and future research directions. Eu-ropean Journal of Information Systems, 31(3), 388-409. https://doi.org/10.1080/0960085X.2021.1927212
Mensah, G. B. (2023). Artificial intelligence and ethics: a comprehensive review of bias mitigation, transparency, and accountability in AI Systems. Preprint, November, 10(1). https://doi.org/10.2196/23218
Militello, L. G., Diiulio, J., Wilson, D. L., Nguyen, K. A., Harle, C. A., Gellad, W., & Lo-Ciganic, W. H. (2025). Using human factors methods to mitigate Bias in artificial intelligence-based clinical decision support. Journal of the American Medical Informatics Association, 32(2), 398–403. https://doi.org/10.1093/jamia/ocae291
Oguntibeju, O. O. (2024). Mitigating artificial intelligence bias in financial systems: A comparative analysis of de-biasing techniques. Asian Journal of Research in Computer Science, 17(12), 165-178. https://doi.org/10.9734/ajrcos/2024/v17i12536
Pant, A., Hoda, R., Tantithamthavorn, C., & Turhan, B. (2024). Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development. arXiv preprint arXiv:2403.15481. https://doi.org/10.48550/arXiv.2403.15481 16
Balayn, A., Lofi, C., & Houben, G. J. (2021). Managing Bias and unfairness in data for decision support: A survey of machine learning and data engineering approaches to identify and mitigate Bias and unfairness within data manage-ment and analytics systems. The VLDB Journal, 30(5), 739–768. https://doi.org/10.1145/1810295.1810326
Zou J, Schiebinger L (2018). AI can be sexist and racist—it’s time to make it fair. Na-ture. https://doi.org/10.1038/d41586-018-05707-8
Hall, P., & Ellis, D. (2023). A systematic review of socio-technical gender bias in AI algorithms. Online Information Review, 47(7), 1264-1279. https://doi.org/10.1108/OIR-08-2021-0452
Cirillo, D., & Rementeria, M. J. (2022). Bias and fairness in machine learning and artificial intelligence. In Sex and gender bias in technology and artificial intelligence (pp. 57-75). Academic Press. https://doi.org/10.1016/B978-0-12-821392-6.00006-6
Sheriff Adefolarin Adepoju, “How machine learning can revolutionize building comfort: Accessing the impact of occupancy prediction models on HVAC control system,” World J. Adv. Res. Rev., vol. 25, no. 1, pp. 2315–2327, Jan. 2025, doi: 10.30574/wjarr.2025.25.1.0161.
Soleimani, M. (2022). Developing unbiased artificial intelligence in recruitment and selection: a processual framework: a dissertation presented in partial fulfillment of the requirements for the degree of doctor of philosophy in management at Massey University, Albany, Auckland, New Zealand (Doctoral dissertation, Massey University). http://hdl.handle.net/10179/17686
Devillers, L., Fogelman-Soulié, F., & Baeza-Yates, R. (2021). AI & human values: Inequalities, biases, fairness, nudge, and feedback loops. Reflections on artificial intelligence for humanity, 76-89. https://doi.org/10.1145/3213765
Cary Jr, M. P., Bessias, S., McCall, J., Pencina, M. J., Grady, S. D., Lytle, K., & Economou‐Zavlanos, N. J. (2025). Em-powering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare. Journal of Nursing Scholarship, 57(1), 130–139. https://doi.org/10.1111/jnu.13007
Desouza, K. C., Dawson, G. S., & Chenok, D. (2020). Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector. Business Horizons, 63(2), 205–213. https://doi.org/10.1016/j.bushor.2019.11.004
Smith, C. J. (2019). Designing trustworthy AI: A human-machine teaming framework to guide development. arXiv preprint arXiv:1910.03515. https://doi.org/10.48550/arXiv.1910.03515
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)

This work is licensed under a Creative Commons Attribution 4.0 International License.
©2024 All rights reserved by the respective authors and JKLST.