Adversarial Robustness and Explainability of Machine Learning Models
Jamil Gafur, Steve Goddard, William Lai · 2024
The rapid advancement of machine learning has brought forth sophisticated neural network models harnessing computational prowess and vast datasets for diverse applications. Nonetheless, with the proliferation of these complex models, apprehensions have surfaced regarding their resilience, interpretability, and biases. To mitigate these concerns, we propose the “Adversarial Observation” framework, amalgamating explainable and adversarial methodologies for comprehensive neural network scrutiny. By integrating explainable techniques, users gain profound insights into the model’s internal mechanisms, fostering transparency and facilitating bias identification. This framework aims to enhance the trustworthiness and accountability of neural network systems amidst their expanding utility.