Abstract: Machine learning technology is continuously evolving and is extensively applied across various domains, demonstrating capabilities beyond human abilities. However, improper use of machine learning methods or biased decision-making can harm human interests, especially in sensitive areas with high-security demand such as finance and healthcare, etc., leading to an increasing attention on the trustworthiness of machine learning. Currently, machine learning technology commonly exhibits several drawbacks, such as biases against underrepresented groups, lack of user privacy protection, lack of model interpretability, and vulnerability to threats and attacks. These shortcomings undermine human trust in machine learning methods. Although researchers have conducted targeted studies on these issues, there is a lack of a comprehensive framework and methodology to systematically provide trustworthy analysis of machine learning. Therefore, this paper reviews the current mainstream definitions, indicators, methods, and evaluations of fairness, interpretability, robustness, and privacy in machine learning. Then, the relationships among these elements are discussed, while a trustworthy machine learning framework is established by integrating an entire lifecycle of machine learning. Finally, we present some of the current issues and challenges awaiting resolution in the field of trustworthy machine learning.

Key words: trustworthy machine learning, fairness, interpretability, robustness, privacy