Trustworthy Machine Learning: From Data to Models
Han, Bo Yao, Jiangchao Liu, Tongliang Li, Bo Koyejo, Sanmi Liu, Feng
Han, Bo
Yao, Jiangchao
Liu, Tongliang
Li, Bo
Koyejo, Sanmi
Liu, Feng
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Department
Machine Learning
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Journal article
Date
2025
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English
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Abstract
The success of machine learning algorithms relies not only on achieving good performance but also on ensuring trustworthiness across diverse applications and scenarios. Trustworthy machine learning seeks to handle critical problems in addressing the issues of robustness, privacy, security, reliability, and other desirable properties. The broad research area has achieved remarkable advancement and brings various emerging topics along with the progress. We present this survey to provide a systematic overview of the research problems under trustworthy machine learning covering the perspectives from data to model. Starting with fundamental data-centric learning, the survey reviews learning with noisy data, long-tailed distribution, out-of-distribution data, and adversarial examples to achieve robustness. Delving into private and secured learning, the survey elaborates on core methodologies differential privacy, different attacking threats, and learning paradigms, to realize privacy protection and enhance security. Finally, it introduces several trendy issues related to the foundation models, including jailbreak prompts, watermarking, and hallucination, as well as causal learning and reasoning. The survey integrates commonly isolated research problems in a unified manner, which provides general problem setups, detailed sub-directions, and further discussion on its challenges or future developments. We hope the comprehensive investigation presented in this survey can serve as a clear introduction for the problem evolution from data to models and also bring new insight for developing trustworthy machine learning.
Citation
B. Han, J. Yao, T. Liu, B. Li, S. Koyejo, and F. Liu, “Trustworthy Machine Learning: From Data to Models,” Foundations and Trends® in Privacy and Security, vol. 7, no. 2–3, pp. 74–246, 2025, doi: 10.1561/3300000043.
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Foundations and Trends® in Privacy and Security
Conference
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