2024/05/20 by Bruno Scarone, Scarone, Bruno, Alfredo Viola +3
Computer Science · Decision Sciences · #Computers and Society (cs.CY) #Data Quality and Management #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning and Data Classification
paper · pdf · doi:10.48550/arxiv.2405.12312
openalex publication_date 2024/05/20 · openalex created_date 2024/05/23 · openalex updated_date 2026/07/28
The widespread use of machine learning and data-driven algorithms for decision making has been steadily increasing over many years. Bias in the data can adversely affect this decision-making. We present a new mitigation strategy to address data bias. Our methods are explainable and come with mathematical guarantees of correctness. They can take advantage of new work on table discovery to find new tuples that can be added to a dataset to create real datasets that are unbiased or less biased. Our framework covers data with non-binary labels and with multiple sensitive attributes. Hence, we are able to measure and mitigate bias that does not appear over a single attribute (or feature), but only intersectionally, when considering a combination of attributes. We evaluate our techniques on publicly available datasets and provide a theoretical analysis of our results, highlighting novel insights into data bias.