A methodology for controlling bias and fairness in synthetic data generation
Open accessAbstract
The development of algorithms, based on machine learning techniques, supporting (or even replacing) human judgment must take into account concepts such as data bias and fairness. Though scientific literature proposes numerous techniques to detect and evaluate these problems, less attention has been dedicated to methods generating intentionally biased datasets, which could be used by data scientists to develop and validate unbiased and fair decision-making algorithms. To this end, this paper presents a novel method to generate a synthetic dataset, where bias can be modeled by using a probabilistic network exploiting structural equation modeling. The proposed methodology has been validated on a simple dataset to highlight the impact of tuning parameters on bias and fairness, as well as on a more realistic example based on a loan approval status dataset. In particular, this methodology requires a limited number of parameters compared to other techniques for generating datasets with a controlled amount of bias and fairness.
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AI systems may replicate social and cultural inequalities embedded in training data, influencing assessment outcomes and learning opportunities.
“Empirical studies show that AI systems may replicate social and cultural inequalities embedded in training data, influencing assessment outcomes and learning opportunities (Baker & Hawn, 2022;Barbierato et al., 2022;Deho et al., 2023;Jiang & Pardos, 2021).”
From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Barbierato et al., 2022
The Introduction and Related Work sections say that real training data can contain bias, including bias rooted in culture, and that algorithms may learn discrimination from datasets. Section 4.5 illustrates potential bias in loan-approval outcomes, but the paper does not address educational assessment outcomes or learning opportunities. The citing sentence extends the paper’s point to an educational setting it did not study. (LLM, conf=0.87)
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Cites
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Cited by
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Memarian, B., & Doleck, T. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100152 · Cited by 416 · see citing works
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149 · see citing works
Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education
Shishakly, R., & Nachouki, M. (2026) · Journal of Information Technology Education: Research · doi:10.28945/5730 · Cited by 4 · see citing works
“Empirical studies show that AI systems may replicate social and cultural inequalities embedded in training data, influencing assessment outcomes and learning opportunities (Baker & Hawn, 2022; Barbierato et al., 2022; Deho et al., 2023; Jiang & Pardos, 2021).”
- Authors
- Barbierato, E., Vedova, M. L. D., Tessera, D., Toti, D., & Vanoli, N.
- Affiliations
- Università Cattolica del Sacro Cuore (IT)
- University of Bergamo (IT)
- Corresponding author
- Barbierato, Enrico
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-05-04
- Volume
- 12
- Issue
- 9
- Pages
- 4619
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4225409112
- Cited by
- 30
- References
- 2
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 7 · 2023: 4
- FWCI
- 7.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- Aisberg (University of Bergamo) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, other-oa)
- Funders
- Catholic University of the Sacred Heart (“Ethic and scientific impacts of AI applications”)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- synthetic data generation, data bias, algorithmic fairness, structural equation modeling, probabilistic graphical models, lending, algorithm validation, fairness evaluation
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