The trustworthiness of machine learning has emerged as a critical topic in the field, encompassing various applications and research areas such as robustness, security, interpretability, and fairness. The last decade saw the development of numerous methods addressing these challenges. In this survey, we systematically review these advancements from a data-centric perspective, highlighting the shortcomings of traditional empirical risk minimization (ERM) training in handling challenges posed by the data. Interestingly, we observe a convergence of these methods, despite being developed independently across trustworthy machine learning subfields. Pearl's hierarchy of causality offers a unifying framework for these techniques. Accordingly, this survey presents the background of trustworthy machine learning development using a unified set of concepts, connects this language to Pearl's causal hierarchy, and finally discusses methods explicitly inspired by causality literature. We provide a unified language with mathematical vocabulary to link these methods across robustness, adversarial robustness, interpretability, and fairness, fostering a more cohesive understanding of the field. Further, we explore the trustworthiness of large pretrained models. After summarizing dominant techniques like fine-tuning, parameter-efficient fine-tuning, prompting, and reinforcement learning with human feedback, we draw connections between them and the standard ERM. This connection allows us to build upon the principled understanding of trustworthy methods, extending it to these new techniques in large pretrained models, paving the way for future methods. Existing methods under this perspective are also reviewed. Lastly, we offer a brief summary of the applications of these methods and discuss potential future aspects related to our survey. For more information, please visit http://trustai.one.

机器学习的可信度是一个重要的话题，涉及到鲁棒性、安全性、可解释性和公平性等各种应用和研究领域。本文系统地从数据中心的角度回顾了这些进展，突出了传统经验风险最小化（ERM）训练处理数据挑战的不足之处，提供了一种统一的语言和数学词汇将这些方法连接起来，促进对该领域的更加协调的理解，并讨论了由因果性文献明确启发的方法。同时，还对大型预训练模型的可信度展开了探讨，并将其与标准ERM进行联系，为未来方法铺平道路。最后，对这些方法的应用和未来潜在方面进行了简要总结和讨论。

迈向可信赖和对齐的机器学习：一个以数据为中心的带因果关系观点的综述