Enforcing fairness in logistic regression algorithm

Sandro Radovanović · Andrija Petrović · Boris Delibašić · Milija Suknović

17 citations0/0 files storedInternational Conference on Innovations in Intelligent Systems and Applications (INISTA)

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Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)Imbalanced Data Classification Techniques

Abstract

Machine learning has been subject to discussion from the legal and ethical points of view in recent years. Automation of the decision-making process can lead to unethical acts with legal consequences. There are examples where the decision made by machine learning systems was unfairly biased toward some group of people. This is mainly because data used for model training were biased and thus developed a predictive model inherited that bias. Therefore, the process of learning a predictive model must be aware and account for the possible bias in the data. In this paper, we propose a modification of the logistic regression algorithm that adds one known and one novel fairness constraints into the process of model learning, thus forcing the predictive model not to create disparate impact and allow equal opportunity for every subpopulation. We demonstrate our model on real-world problems and show that a small reduction in predictive performance can yield a high improvement in disparate impact and equality of opportunity.

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References

2014Big Data's Disparate Impact 2016Equality of Opportunity in Supervised Learning 2015THE THEORY OF DISCRIMINATION 2018Mitigating Unwanted Biases with Adversarial Learning 2011Data preprocessing techniques for classification without discrimination 2017Algorithmic Decision Making and the Cost of Fairness 2012Fairness-Aware Classifier with Prejudice Remover Regularizer 2005Racism Without Racists: Colorblind Racism and the Persistence of Racial Inequality in the United States

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2023Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods 2025A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models 2021A fair classifier chain for multi‐label bank marketing strategy classification

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