In the realm of data-driven systems, understanding and controlling biases in datasets emerges as a critical challenge. These biases, defined in this study as systematic discrepancies, have the potential to skew algorithmic outcomes and even compromise data privacy. Mutual information serves as a key tool in the analysis, discerning both direct and indirect relationships between variables. Utilizing structural equation modeling, this paper introduces a synthetic dataset generation method founded on a two-step optimization algorithm that aims to fine-tune variable relationships and achieve targeted mutual information levels between attribute pairs. The algorithm's first phase utilizes gradient-less optimization, focusing on individual variables. The subsequent phase harnesses gradient-based methods to unravel deeper variable interdependencies. The approach is dual-purpose: it refines existing datasets for bias mitigation and creates synthetic datasets with defined bias levels, addressing a crucial research gap. Two case studies showcase the methodology. One emphasizes the finesse of network parameter adjustments in a simulated setting. The other applies the methodology to a realistic job hiring dataset, effectively reducing bias while safeguarding key variable relationships. In summary, this paper offers a novel method for bias management, presents tools for quantitative bias adjustments, and provides evidence of the method's broad applicability through varied use cases.

Barbierato, E., Pozzi, A., Tessera, D., Controlling Bias Between Categorical Attributes in Datasets: A Two-Step Optimization Algorithm Leveraging Structural Equation Modeling, <<IEEE ACCESS>>, N/A; 11 (N/A): 115493-115510. [doi:10.1109/ACCESS.2023.3325235] [https://hdl.handle.net/10807/271838]

Controlling Bias Between Categorical Attributes in Datasets: A Two-Step Optimization Algorithm Leveraging Structural Equation Modeling

Barbierato, Enrico
Membro del Collaboration Group
;
Tessera, Daniele
Membro del Collaboration Group
2023

Abstract

In the realm of data-driven systems, understanding and controlling biases in datasets emerges as a critical challenge. These biases, defined in this study as systematic discrepancies, have the potential to skew algorithmic outcomes and even compromise data privacy. Mutual information serves as a key tool in the analysis, discerning both direct and indirect relationships between variables. Utilizing structural equation modeling, this paper introduces a synthetic dataset generation method founded on a two-step optimization algorithm that aims to fine-tune variable relationships and achieve targeted mutual information levels between attribute pairs. The algorithm's first phase utilizes gradient-less optimization, focusing on individual variables. The subsequent phase harnesses gradient-based methods to unravel deeper variable interdependencies. The approach is dual-purpose: it refines existing datasets for bias mitigation and creates synthetic datasets with defined bias levels, addressing a crucial research gap. Two case studies showcase the methodology. One emphasizes the finesse of network parameter adjustments in a simulated setting. The other applies the methodology to a realistic job hiring dataset, effectively reducing bias while safeguarding key variable relationships. In summary, this paper offers a novel method for bias management, presents tools for quantitative bias adjustments, and provides evidence of the method's broad applicability through varied use cases.
2023
Inglese
Barbierato, E., Pozzi, A., Tessera, D., Controlling Bias Between Categorical Attributes in Datasets: A Two-Step Optimization Algorithm Leveraging Structural Equation Modeling, <<IEEE ACCESS>>, N/A; 11 (N/A): 115493-115510. [doi:10.1109/ACCESS.2023.3325235] [https://hdl.handle.net/10807/271838]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/271838
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