Responsible Ai
Responsible AI and AutoML: Studying the Impact of Missing Data on Group Fairness
- responsible AI
- fairness-aware machine learning
- group fairness
- missing data imputation
- AutoML research
- MLJAR AutoML
- mljar-supervised
- machine learning fairness
- data preprocessing
- data-centric AI
- bias in machine learning
- AI ethics
- fair AI systems
- artificial intelligence research
MLJAR tools were used in the following publication.
Exploring the Influence of Missing Data Imputation in Group Fairness Metrics
Arthur Dantas Mangussi, Ricardo Cardoso Pereira, Miriam Seoane Santos, Ana Carolina Lorena, Mykola Pechenizkiy, Pedro Henriques Abreu
This research investigates how missing-data imputation affects group fairness in machine learning systems. The study uses mljar-supervised as part of its experimental machine learning workflow to evaluate how preprocessing choices, classifiers, and missing-data mechanisms influence fairness metrics. By analyzing the interaction between missing values, imputation strategies, and predictive models, the work helps researchers and practitioners understand how technical decisions in data preparation can affect different groups of people. This case study illustrates how MLJAR AutoML can support responsible AI research by helping build and evaluate machine learning pipelines that are not only accurate, but also more transparent, reproducible, and socially responsible.
Artificial Intelligence • May 1, 2026
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