A Comparative Analysis of Logistic Regression and Random Forest for Individual Fairness in Machine Learning
Year: 2025
Type: article
Abstract: In high-stakes domains such as finance, healthcare, and criminal justice, machine learning (ML) systems must balance predictive performance with fairness and transparency. This paper presents a comparative analysis of two widely used ML models, logistic regression and random forest, evaluated through the lens of individual fairness. Using the UCI Adult Income and COMPAS datasets, we assess performance in terms of accuracy, F1 score, individual consistency, and disparate treatment. Our findings indicate that while random forests offer marginally higher accuracy (by approximately 1%), logistic regression improves individual consistency by up to 4%, suggesting it is preferable in fairness-sensitive applications. This study emphasizes model selection’s role in achieving ethically responsible AI.
Author: Sanjit Kumar Saha
Institution: Jahangirnagar University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4410716831