执法
计算机科学
数字取证
点(几何)
网络犯罪
数据科学
数字证据
人工智能
计算机安全
万维网
互联网
法学
几何学
政治学
数学
作者
Stuart W. Hall,Amin Sakzad,Kim–Kwang Raymond Choo
摘要
Abstract EXplainable artificial intelligence (XAI) is an emerging research area relating to the creation of machine learning algorithms from which explanations for outputs are provided. In many fields, such as law enforcement, it is necessary that decisions made by and with the assistance of artificial intelligence (AI)‐based tools can be justified and explained to a human. We seek to explore the potential of XAI to further enhance triage and analysis of digital forensic evidence, using examples of the current state of the art as a starting point. This opinion letter will discuss both practical and novel ideas as well as controversial points for leveraging XAI to improve the efficacy of digital forensic (DF) analysis and extract forensically sound pieces of evidence (also known as artifacts) that could be used to assist investigations and potentially in a court of law. This article is categorized under: Digital and Multimedia Science > Artificial Intelligence Digital and Multimedia Science > Cybercrime Investigation
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