Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions
Open accessAbstract
Artificial Intelligence (AI) has been used extensively in automatic decision making in a broad variety of scenarios, ranging from credit ratings for loans to recommendations of movies. Traditional design guidelines for AI models focus essentially on accuracy maximization, but recent work has shown that economically irrational and socially unacceptable scenarios of discrimination and unfairness are likely to arise unless these issues are explicitly addressed. This undesirable behavior has several possible sources, such as biased datasets used for training that may not be detected in black-box models. After pointing out connections between such bias of AI and the problem of induction, we focus on Popper's contributions after Hume's, which offer a logical theory of preferences. An AI model can be preferred over others on purely rational grounds after one or more attempts at refutation based on accuracy and fairness. Inspired by such epistemological principles, this paper proposes a structured approach to mitigate discrimination and unfairness caused by bias in AI systems. In the proposed computational framework, models are selected and enhanced after attempts at refutation. To illustrate our discussion, we focus on hiring decision scenarios where an AI system filters in which job applicants should go to the interview phase.
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Self-supervised learning employs self-prediction strategies inspired by NLP, using techniques like masking for input reconstruction, significantly enhanced by recent Vision Transformers developments.
“Further, it employs self-prediction strategies, inspired by NLP, using techniques like masking for input reconstruction, significantly enhanced by recent Vision Transformers developments [249], [250], [165].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [249]
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Cites
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
Objective Knowledge: An Evolutionary Approach
Popper, S. K. (1972) · DigitalGeorgetown (Georgetown University Library) · Cited by 702 · see citing works
Fairness Constraints: Mechanisms for Fair Classification
Zafar, M. B., Valera, I., Rodriguez, M. G., & Gummadi, K. P. (2015) · arXiv · doi:10.48550/arxiv.1507.05259 · Cited by 645 · see citing works
Logic-based Benders decomposition
Hooker, J., & Ottosson, G. (2003) · Mathematical Programming · doi:10.1007/s10107-003-0375-9 · Cited by 601 · see citing works
Embedding Ethical Principles in Collective Decision Support Systems
Greene, J. D., Rossi, F., Tasioulas, J., Venable, K. B., & Williams, B. C. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9804 · Cited by 64 · see citing works
Integrating induction and deduction for finding evidence of discrimination
Pedreschi, D., Ruggieri, S., & Turini, F. (2009) · Proceedings of the 12th International Conference on Artificial Intelligence and Law · doi:10.1145/1568234.1568252 · Cited by 55 · see citing works
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Köchling, A., & Wehner, M. C. (2020) · Business Research · doi:10.1007/s40685-020-00134-w · Cited by 511 · see citing works
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Hunkenschroer, A. L., & Luetge, C. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05049-6 · Cited by 447 · see citing works
The Forgotten Margins of AI Ethics
Birhane, A., Ruane, E., Laurent, T., Brown, M. S., Flowers, J., Ventresque, A., & Dancy, C. L. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533157 · Cited by 123 · see citing works
Algorithmic equity in the hiring of underrepresented IT job candidates
Yarger, L., Cobb Payton, F., & Neupane, B. (2019) · Online Information Review · doi:10.1108/oir-10-2018-0334 · Cited by 122 · see citing works
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Hunkenschroer, A. L., & Kriebitz, A. (2023) · AI and Ethics · doi:10.1007/s43681-022-00166-4 · Cited by 111 · see citing works
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
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People versus machines: introducing the HIRE framework
Will, P., Krpan, D., & Lordan, G. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10193-6 · Cited by 53 · see citing works
Artificial intelligence (AI) and ethical concerns: a review and research agenda
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Master and Slave: the Dialectic of Human-Artificial Intelligence Engagement
Kim, T. W., Maimone, F., Pattit, K., Sisón, A. J. G., & Teehankee, B. L. (2021) · Humanistic Management Journal · doi:10.1007/s41463-021-00118-w · Cited by 27 · see citing works
Diversity and future of work: inequality abound or opportunities for all?
Özkazanç‐Pan, B. (2019) · Management Decision · doi:10.1108/md-02-2019-0244 · Cited by 29 · see citing works
Intelligent career planning via stochastic subsampling reinforcement learning
Guo, P., Xiao, K., Ye, Z., Zhu, H., & Zhu, W. (2022) · Scientific Reports · doi:10.1038/s41598-022-11872-8 · Cited by 34 · see citing works
Gauthier, A., Rizvi, S., Cukurova, M., & Mavrikis, M. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.994967 · Cited by 23 · see citing works
Can AI close the gender gap in the job market? Individuals' preferences for AI evaluations
Steinberg, A. L. S., & Hohenberger, C. (2023) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2023.100287 · Cited by 24 · see citing works
Hunkenschroer, A. (2021) · AI Ethics Journal · doi:10.47289/aiej20210716-3 · Cited by 18 · see citing works
Assessing and Mitigating Bias in Artificial Intelligence: A Review
Sinha, A., Sapra, D., Sinwar, D., Singh, V., & Raghuwanshi, G. (2023) · Recent Advances in Computer Science and Communications · doi:10.2174/2666255816666230523114425 · Cited by 18 · see citing works
Dalal, D. K., Sassaman, L., & Zhu, X. (2020) · Personnel Assessment and Decisions · doi:10.25035/pad.2020.02.007 · Cited by 16 · see citing works
Trusted Artificial Intelligence: Technique Requirements and Best Practices
Zhang, T., Qin, Y., & Li, Q. (2021) · International Conference on Cyberworlds (CW) · doi:10.1109/cw52790.2021.00058 · Cited by 14 · see citing works
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Montag, C., Riazi, A. M., Mikros, G. K., Becker, B., & Ali, R. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124222 · Cited by 13 · see citing works
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Martínez-Rolán, X., Valencia, J. M. C., & Piñeiro-Otero, T. (2023) · Management and industrial engineering · doi:10.1007/978-3-031-42060-3_5 · Cited by 11 · see citing works
Bias Analysis in Healthcare Time Series (BAHT) Decision Support Systems from Meta Data
Dakshit, S., Dakshit, S., Khargonkar, N., & Prabhakaran, B. (2023) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-023-00133-6 · Cited by 8 · see citing works
Trusted Artificial Intelligence: Technique Requirements and Best Practices
Zhang, T., Qin, Y., & Li, Q. (2021) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom53373.2021.00207 · Cited by 6 · see citing works
Jantzen, L., Bottel, M., & Kempen, R. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.408 · Cited by 5 · see citing works
Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic
de Sá, Â. A. R., Carvalho, J. D., & Naves, E. L. M. (2021) · AI & Society · doi:10.1007/s00146-021-01315-9 · Cited by 3 · see citing works
Why Did the Robot Cross the Road?
Taschdjian, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60117-1_39 · Cited by 3 · see citing works
Artificial Intelligence in Modern Human Resources Practice
Sposato, M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6507-6.ch009 · Cited by 2 · see citing works
AI-Driven Evolution in HR: A Bibliometric Study on Professional Roles and Skill Adaptation
Wadhwa, S., Sharma, A., & Ahalawat, K. (2025) · International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) · doi:10.1109/cictn64563.2025.10932599 · Cited by 2 · see citing works
Charting the Ethical Landscape
Pria, S., Rubaie, I. A., & Prasad, B. V. V. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6215-0.ch015 · Cited by 2 · see citing works
Effective Strategies for Mitigating Bias in Hiring Algorithms: A Comparative Analysis
Jazakallah, Y. (2023) · Journal of Artificial Intelligence Machine Learning and Data Science · doi:10.51219/jaimld/yusuf-jazakallah/16 · Cited by 2 · see citing works
The Falsificationist View of Machine Learning
Reizinger, P. (2023) · Információs Társadalom · doi:10.22503/inftars.xxiii.2023.2.7 · Cited by 1 · see citing works
Krishna, Y., & Kandasamy, L. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-06099-0_11 · Cited by 1 · see citing works
Learning in a Hiring Logic and Optimal Contracts
Sales, J. I., & Ramos, F. D. S. (2021) · IEEE Access · doi:10.1109/access.2021.3128039 · Cited by 1 · see citing works
Professionals’ Opinions on the Use of AI Software for HRM
Azad, N., Hina, M., & Islam, A. K. M. N. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-85849-9_11
Wiam, L., CHRAIBI, H., & CHRAIBI, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-86698-2_10
Qabli, M. E., Ammari, F., & Boumahdi, A. (2025) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc66714.2025.11135175
Bias Detection and Mitigation in AI-Driven Recruitment Systems
Assafuah, G., Turner, C., Turner, C. B., & Nwosu, K. C. (2026) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet65726.2026.11633126
- Authors
- Vasconcelos, M., Cardonha, C., & Gonçalves, B.
- Affiliations
- IBM (Brazil) (BR)
- ORCID
- Cardonha, Carlos
- Venue
- Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2018-12-27
- Pages
- 323-329
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2770852382
- Cited by
- 38
- References
- 7
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 2 · 2023: 7 · 2022: 6 · 2021: 7 · 2020: 4 · 2019: 2
- FWCI
- 5.75
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- bias mitigation, algorithmic discrimination, epistemology of AI, problem of induction, Karl Popper, David Hume, refutation, model selection, fairness, hiring decisions, automated decision-making, biased training data, black-box models
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