Missing Data Under Green AI Umbrella - Mangussi, Arthur Dantas; Pereira, Ricardo Cardoso; Abreu, Pedro Henriques; Lorena, Ana Carolina (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2025.14312, 2025)
Missing data is a common issue that can undermine machine learning performance, and imputation methods have emerged as state-of-the-art solutions. However, training these methods can be costly and environmentally impactful. In this work, we investigate the missing data problem under Green AI constraints using a Data-Centric AI approach. We evaluate three missingness mechanisms, four missing rates, and ten datasets to assess both data quality and downstream performance. We also propose an optimization model to select the best-performing imputation method while considering sustainability constraints, offering a path toward more responsible and effective data imputation.