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BiasTrap: Runtime Detection of Biased Prediction in Machine Learning Systems
Machine Learning (ML) systems are now widely used across various fields such as hiring, healthcare, and criminal justice, but they are prone to unfairness and discrimination, which can have serious consequences for individuals and society. Although various fairness testing methods have been developed to tackle this issue, they lack the mechanism to monitor ML system behaviour at runtime continuously. This study proposes a runtime verification tool called BiasTrap to detect and prevent discrimination in ML systems. The tool combines data augmentation and bias detection components to create and analyse instances with different sensitive attributes, enabling the detection of discriminatory behaviour in the ML model. The simulation results demonstrate that BiasTrap can effectively detect discriminatory behaviour in ML models trained on different datasets using various algorithms. Therefore, BiasTrap is a valuable tool for ensuring fairness in ML systems in real time. © 2024, Semarak Ilmu Publishing. All rights reserved.
Journal of Advanced Research in Applied Sciences and Engineering Technology
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