Mitigating Unwanted Biases with Adversarial Learning
Open accessAbstract
Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. The objective is to maximize the predictor's ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of gradient-based learning models, including both regression and classification tasks.
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Bias mitigation techniques in the literature are classified into pre-processing, in-processing, and post-processing.
“Several techniques exist within the literature, classified in the following categories:”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [397]
The Related Work section describes several debiasing techniques, supporting the claim that techniques exist in the literature. It does not classify them as pre-processing, in-processing, and post-processing; searches for those terms found no such classification in our copy. No page number was available. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAdversarial learning can simultaneously learn a predictor and an adversary that models a protected variable, and by minimizing the adversary capacity to predict this protected variable, accurate predictions can show less stereotyping of the protected variable, almost achieving equality of odds as a fairness notion.
“Here, adversarial learning [81] can simultaneously learn a predictor and an adversary that models a protected variable, and by minimizing the adversary capacity to predict this protected variable, accurate predictions can show less stereotyping of the protected variable, almost achieving equality of odds as a fairness notion.”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [81]
Such de-biasing approaches force the model to account for underrepresented groups and achieve better performance when applied to them.
“Such approaches force the model to account for underrepresented groups and achieve a better performance when applied to them.”
From Mitigating bias in machine learning for medicine (2021) · cites this paper as [9]
Mathematical approaches for de-biasing, such as adversarial de-biasing or oversampling, mitigate the risk of bias at the model development step.
“Beyond creating diverse datasets for model development, there are mathematical approaches for de-biasing that mitigate the risk of bias at this step, such as adversarial de-biasing9 or oversampling10.”
From Mitigating bias in machine learning for medicine (2021) · cites this paper as [9]
Recent developments in bias detection and mitigation include adopting re-sampling, adversarial learning, and open-source toolkits such as IBM AI Fairness 360 (AIF360) and Aequitas.
“Recent developments in bias detection and mitigation also include methods such as adopting re-sampling118, adversarial learning119, and open-source toolkits such as IBM AI Fairness 360 (AIF360) (aif360.mybluemix.net) and Aequitas (dsapp.uchicago.edu/projects/aequitas).”
From Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare (2020) · cites this paper as [119]
Adversarial learning with variable of group learning was presented to mitigate undesired bias from a well-trained model.
“Adversarial learning along with variable of group learning was presented to mitigate undesired bias from the well trained model [161].”
From Artificial intelligence bias in medical system designs: a systematic review (2023) · cites this paper as [161]
Algorithmic bias can be addressed in the prediction algorithm itself through custom regularization constraints or cost functions, or adversarial learning algorithms.
“Algorithmic bias can also be addressed in the prediction algorithm itself through the use of custom regularization constraints or cost functions which determine the relative “cost” of making an incorrect decision (Kamishima et al., 2012) or adversarial learning algorithms can be used to minimize the bias (Zhang et al., 2018).”
From Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health (2021) · cites this paper as Zhang et al., 2018
These methods have been successfully shown to achieve equitable subgroup performance while maintaining or even improving overall model performance.
“These methods have been successfully shown to achieve equitable subgroup performance while maintaining or even improving overall model performance [107,108], demonstrating that bias mitigation need not be at the expense of overall performance.”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [107]
Federated learning enables institutions to collaborate on model training without sharing sensitive patient data.
“Federated learning is a powerful solution that enables institutions to collaborate on model training without sharing sensitive patient data.”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [121]
Federated learning successfully develops algorithms that maintain accuracy across diverse groups.
“This approach successfully develops algorithms that maintain accuracy across diverse groups [120,121,122].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [121]
Most approaches in adversarial fairness use notions of fairness within the adversary to apply feedback for model tuning as a form of in-processing where the adversary penalizes the model if a sensitive variable is predictable from the dependent variable.
“Most approaches in this area use notions of fairness within the adversary to apply feedback for model tuning as a form of in-processing where the adversary penalizes the model if a sensitive variable is predictable from the dependent variable (e.g.[28,30,58,91,289,299,305,314]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [314]
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A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes
Huang, Y., Guo, J., Donahoo, W. T., Lee, Y. A., Fan, Z., Lu, Y., Chen, W.-H., Tang, H., Bilello, L., Saguil, A. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52960-9 · Cited by 14 · see citing works
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works
Generating Fair Universal Representations using Adversarial Models
Kairouz, P., Liao, J., Huang, C., Vyas, M., Welfert, M., & Sankar, L. (2019) · arXiv · doi:10.48550/arxiv.1910.00411 · Cited by 19 · see citing works
- Authors
- Zhang, B. H., Lemoine, B., & Mitchell, M.
- Affiliations
- Stanford University (US)
- Google (United States) (US)
- Venue
- Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2018-12-27
- Pages
- 335-340
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2784397426
- Cited by
- 1,387
- References
- 10
- Citations by year
- 2026: 133 · 2025: 223 · 2024: 210 · 2023: 198 · 2022: 184 · 2021: 208 · 2020: 140 · 2019: 73 · 2018: 18
- FWCI
- 196.47
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.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 › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- adversarial learning, algorithmic fairness, protected characteristics, census classification, gender bias, stereotyping
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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