Provenance Documentation to Enable Explainable and Trustworthy AI:A Literature Review  

作　　者：Amruta Kale Tin Nguyen Frederick C.Harris Jr. Chenhao Li Jiyin Zhang Xiaogang Ma 

机构地区：[1]Department of Computer Science,Universityof Idaho,Moscow,ID83844,USA [2]Department of Computer Science and Engineering,University of Nevada,Reno,Reno,NV 89557,USA

出　　处：《Data Intelligence》2023年第1期139-162,共24页数据智能（英文）

基　　金：supported by the National Science Foundation under Grants No.2019609;the National Aeronautics and Space Administration under Grant No.80NSSC21M0028.

摘　　要：Recently artificial intelligence(AI)and machine learning(ML)models have demonstrated remarkable progress with applications developed in various domains.It is also increasingly discussed that AI and ML models and applications should be transparent,explainable,and trustworthy.Accordingly,the field of Explainable AI(XAI)is expanding rapidly.XAI holds substantial promise for improving trust and transparency in AI-based systems by explaining how complex models such as the deep neural network(DNN)produces their outcomes.Moreover,many researchers and practitioners consider that using provenance to explain these complex models will help improve transparency in AI-based systems.In this paper,we conduct a systematic literature review of provenance,XAI,and trustworthy AI(TAI)to explain the fundamental concepts and illustrate the potential of using provenance as a medium to help accomplish explainability in AI-based systems.Moreover,we also discuss the patterns of recent developments in this area and offer a vision for research in the near future.We hope this literature review will serve as a starting point for scholars and practitioners interested in learning about essential components of provenance,XAI,and TAI.

关 键 词：Explainable AI Trustworthy AI Provenance documentation Workflow platforms Data science 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]