Trustworthy Machine Learning in the Era of Foundation Models

This position article examines trustworthy machine learning with foundation models by investigating the four essential aspects. In learning, we describe how pretraining, fine-tuning, and reinforcement learning enable models to acquire generalizable knowledge and emphasize the importance of high-quality, unbiased training data, as well as robust training methods. In reasoning, we summarize the fundamental methodology of training-free, post-training, and test-time scaling methods that enhance logical deduction, reasoning transparency, and systematic safety. In planning, we incorporate neurosymbolic methods that combine adaptable neural capabilities with formally verifiable symbolic reasoning, ensuring safe and accountable decision making. In multimodality, we investigate the need for multimodal integration, where aligning information from different sensory input sources is important to mitigate biases and errors. The article presents an interdisciplinary vision for incorporating capability, robustness, safety, and explainability to establish trustworthy foundation models, paving the way for their reliable deployment in real-world applications, such as financial and clinical decision making.