工业工程2024，Vol.27Issue(2)：14-26,13.DOI:10.3969/j.issn.1007-7375.230241
可信机器学习综述
A Review of Trustworthy Machine Learning
摘要
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.关键词
可信机器学习/公平性/可解释性/鲁棒性/隐私Key words
trustworthy machine learning/fairness/interpretability/robustness/privacy分类
管理科学引用本文复制引用
陈彩华,佘程熙,王庆阳..可信机器学习综述[J].工业工程,2024,27(2):14-26,13.基金项目
国家自然科学基金优秀青年科学基金资助项目(12122107) （12122107）
