Strategies Selection for a Fair Classification in Logistic Regression: A Comparative Analysis - V. Pinheiro, Murilo; M. Silva, Maria de Lourdes; Machado, Javam C. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2023: Anais Estendidos do XXXVIII Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd_estendido.2023.232722, 2023)
The increasing use of technology leads society to a new concern: the use of machine learning models on personal data and the potentially biased classification. A new definition, called fairness, emerged to mitigate and combat discrimination in algorithms. Fairness literature includes several techniques to guarantee fair outputs for different demographic groups. There are three phases where an algorithm can achieve fairness. We explore some methods of each stage to construct a comparative analysis that evaluates fairness and utility metrics. Our analysis aims to understand the many ways to achieve fairness using logistic regression in the three most popular datasets in fairness literature. We include several experiments to compare five fairness techniques and select the best for each application.