# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 1 Introduction  
In recent years, the application of machine learning (ML) systems to decision-making in sensitive areas such as healthcare, finance, employment, and criminal justice has raised considerable controversy concerning algorithmic fairness. As demonstrated in [1], cases such as the COMPAS recidivism algorithm and Amazon's hiring algorithm illustrate how AI systems can fall short of fairness, accountability, and transparency, leading to ethical concerns.  
Machine learning models, while dominating prediction performance and interpretability, are known to inherit and amplify existing biases in historical data and generate unfair outcomes. This has resulted in mounting research that aims to make such models more fair by developing fairness-aware interventions that minimize disparate effects across demographic groups with no detectable drop in accuracy [2].  
The domain of algorithmic fairness has seen tremendous growth in the past decade, spurred by increased consciousness of the social consequences of machine learning (ML) systems. Fairness-aware machine learning arose in reaction to egregious cases of algorithmic bias in areas such as criminal justice, finance, and health. Initial attempts--such as the formulation of demographic parity and equalized odds circa 2016--sought to establish formal fairness definitions for evaluating and curbing discrimination. These approachestypically seek to alter model behavior so that results are balanced across sensitive attributes such as race, gender, or age. However, fairness for ML is a contentious and complex concept; models satisfying one definition of fairness may violate another. Thus, fairness-aware variants of classic ML algorithms are rather more than mere optimizations to justice, but trade-off systems that mesh accuracy, parity, and explainability.  
Though various fairness-aware approaches have been proposed, including preprocessing approaches, in-processing algorithmic recasting, and post-processing modifications, practical effect and relative performance across different models and datasets are little explored. A careful examination of such variants must be conducted in order to enlighten practitioners and policymakers regarding the appropriate approaches for practical use. Much of the literature also revolves around deep learning or domain-specific pipelines, excluding the ongoing relevance of classical models such as Logistic Regression, Support Vector Machines, Decision Trees, and k-nearest neighbors to a wide range of practical uses.  
This paper provides a comprehensive review and empirical comparison of fairness-aware variants of standard machine learning models. We examine their theoretical underpinnings, cluster them according to intervention strategy, and compare their performance across multiple fairness metrics and datasets across different application domains.  
In Section 2, we present various fairness-aware modifications of the Naive Bayes algorithm. Section 3 explores fairness-enhanced adaptations of Decision Trees, one of the most interpretable machine learning models. Fairness-aware variants of Logistic Regression and a modified version of Support Vector Machine are discussed in Section 4. Finally, fairness-aware ensemble models and combination of exponentiated learning with three different machine learning algorithms, Naive Bayes, Decision Tree and Logistic Regression, are examined in Sections 5 and 6. Section 7 provides a detailed account of the experimental procedure, systematically examining the datasets employed, the configuration of model parameters, and the evaluation metrics used to assess both predictive performance and fairness. The results presented in Section 8 cover all fairness-aware variants of classical machine learning algorithms across each dataset. The final sections of the paper include a comprehensive discussion of the results (Section 9) and a summary of the main conclusions (Section 10).