Trustworthy machine learning has become increasingly critical as AI systems are integrated into high-stakes sectors like healthcare, criminal justice, and finance. Ensuring these systems are reliable, transparent, and fair is essential to prevent biases, enhance security, and maintain public trust. Recent incidents, such as AI models exhibiting discriminatory behavior or being vulnerable to adversarial attacks, highlight the urgent need for robust and ethical AI practices. As AI continues to influence significant societal decisions, prioritizing trustworthiness in machine learning is imperative to safeguard against potential harms and ensure equitable outcomes.

This seminar series aim to share the latest findings in the trustworthy machine learning field and exchange ideas with external invited speakers from academia or industry. Welcome to join, and let's explore the science boundary in modern machine learning.