Building Trustworthy AI from the view of Adversarial Robustness and Explainability
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As artificial intelligence (AI) systems become increasingly integrated into safety-critical and high-stakes domains, ensuring their trustworthiness has emerged as a central research challenge. Two foundational pillars of trustworthy AI are adversarial robustness and explainability. Adversarial robustness addresses the vulnerability of machine learning models to carefully crafted perturbations that can cause erroneous or manipulated outputs, exposing critical weaknesses in reliability and security. Explainability, on the other hand, seeks to make AI systems transparent and interpretable, enabling stakeholders to understand, validate, and contest model decisions. In this presentation, I will introduce my research framed around these two core pillars.