Exploring Trustworthy Machine Learning from a Broader Perspective: Advancements and Insights
Machine learning (ML) has transformed numerous domains, demonstrating exceptional per-
performance in autonomous driving, medical diagnosis, and decision-making tasks. Nevertheless, ensuring the trustworthiness of ML models remains a persistent challenge, particularly with the emergence of new applications. The primary challenges in this context are the selection of an appropriate solution from a multitude of options, mitigating adversarial attacks, and advancing towards a unified solution that can be applied universally.
The thesis comprises three interconnected parts, all contributing to the overarching goal of improving trustworthiness in machine learning. Firstly, it introduces an automated machine learning (AutoML) framework that streamlines the training process, achieving optimum performance, and incorporating existing solutions for handling trustworthiness concerns. Secondly, it focuses on enhancing the robustness of machine learning models, particularly against adversarial attacks. A robust detector named "Argos" is introduced as a defense mechanism, leveraging the concept of two "souls" within adversarial instances to ensure robustness against unknown attacks. It incorporates the visually unchanged content representing the true label and the added invisible perturbation corresponding to the misclassified label. Thirdly, the thesis explores the realm of causal ML, which plays a fundamental role in assisting decision-makers and addressing challenges such as interpretability and fairness in traditional ML. By overcoming the difficulties posed by selective confounding in real-world scenarios, the proposed scheme utilizes dual-treatment samples and two-step procedures with counterfactual predictors to learn causal relationships from observed data. The effectiveness of the proposed scheme is supported by theoretical error bounds and empirical evidence using synthetic and real-world child placement data. By reducing the requirement for observed confounders, the applicability of causal ML is enhanced, contributing to the overall trustworthiness of machine learning systems.