Autor(es)

Inocencio Junior, Ronaldo Lopes ; Basgalupp, Marcio P. ; Ludermir, Teresa B. ; Lorena, Ana Carolina

• Tipo de documento: Artigo Científico
• Fonte: 40TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING; v. N/A, p. 9-pg., 2025-01-01.

Resumo

We increasingly integrate technology into our daily activities, and using Machine Learning (ML) algorithms in various domains has become a common practice. However, in crucial sectors where algorithmic decisions significantly impact people's lives, there is a need to scrutinize these decisions more carefully. Using these algorithms in critical areas, such as courtrooms, raises concerns about potential bias and prejudice, directly affecting the justice and partiality of these tools. There is an urge to create algorithms supporting ethical decisions. This paper proposes using data balancing techniques to mitigate the sample bias present in datasets, aiming to make subsequent ML algorithm training more impartial. A version of the ADASYN algorithm is developed, which performs data balancing at both the class level and at the level of protected attributes, enhancing the diversity and representativeness of the protected groups in the datasets. Experimental results show the technique can promote greater fairness in the predictions of different ML models while keeping a good trade-off with overall accuracy. (AU)

• Processo FAPESP: 22/07458-1 - Construção e seleção automática de algoritmos de aprendizado de máquina
• Beneficiário: Márcio Porto Basgalupp
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 21/06870-3 - Além da seleção de algoritmos: meta-aprendizado para análise e entendimento de dados e algoritmos
• Beneficiário: Ana Carolina Lorena
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2
• Processo FAPESP: 20/09835-1 - IARA - Inteligência Artificial Recriando Ambientes
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada