Experimental study on algorithmic fairness applied to credit analysis models

• Tiago A. Oliveira Federal University of Bahia (UFBA)
• João V. L. Oliveira Federal University of Bahia (UFBA)
• Tarcísio P. Farias Federal Institute of Education, Science, and Technology of Bahia (IFBA)
• Erick W. R. Cruz Federal Institute of Education, Science, and Technology of Bahia (IFBA)
• Leandro J. S. Andrade Federal University of Bahia (UFBA)
• Robespierre Pita Federal University of Bahia (UFBA)

DOI: https://doi.org/10.5753/sbbd_estendido.2024.243797

Abstract

Machine Learning (ML) models for algorithmic decision-making are widely applied to support risk management and credit analysis. However, the significant increase in available data, the complexity of modern models, and public scrutiny surrounding artificial intelligence have intensified the debate on the need to identify and mitigate biases in predictions. This study aims to analyze the relationship between quantitative measures of algorithmic fairness and quality metrics obtained by ML models in credit analysis tasks. Initial results indicate that certain models can achieve promising performance levels without necessarily affecting or deteriorating fairness in their predictions.

Keywords: justiça algoritmica, machine learning, análise de crédito

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