可解释人工智能技术实用教程

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-06-12 DOI:10.1145/3670685
Adrien Bennetot, Ivan Donadello, Ayoub El Qadi El Haouari, Mauro Dragoni, Thomas Frossard, Benedikt Wagner, Anna Sarranti, Silvia Tulli, Maria Trocan, Raja Chatila, Andreas Holzinger, Artur d'Avila Garcez, Natalia Díaz-Rodríguez
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引用次数: 0

摘要

过去几年,不透明的自动决策支持系统(如深度神经网络(DNN))急剧增加。虽然 DNNs 具有强大的泛化和预测能力,但很难获得对其行为的详细解释。由于不透明的机器学习模型越来越多地被用于在关键领域进行重要预测,因此存在着创建和使用不合理或不合法决策的危险。因此,人们普遍认同赋予 DNN 可解释性的重要性。可解释人工智能(XAI)技术可用于验证和认证模型输出,并通过可信、负责、透明和公平等理想概念来增强模型输出。本指南旨在为具有计算机科学背景、希望从机器学习模型中获得直观见解并辅以开箱即用的解释的人提供实用手册。文章旨在通过在特定的日常模型、数据集和用例中应用 XAI 技术,纠正缺乏实用 XAI 指南的问题。在每一章中,读者都会看到对所提方法的描述,以及一个或几个使用 Python 笔记本的示例。这些示例可以很容易地进行修改,以便应用于特定的应用。我们还解释了使用每种技术的前提条件、用户将学习到的知识以及它们针对的任务。
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A Practical tutorial on Explainable AI Techniques

The past years have been characterized by an upsurge in opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although DNNs have great generalization and prediction abilities, it is difficult to obtain detailed explanations for their behaviour. As opaque Machine Learning models are increasingly being employed to make important predictions in critical domains, there is a danger of creating and using decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing DNNs with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is intended to be the go-to handbook for anyone with a computer science background aiming to obtain an intuitive insight from Machine Learning models accompanied by explanations out-of-the-box. The article aims to rectify the lack of a practical XAI guide by applying XAI techniques in particular day-to-day models, datasets and use-cases. In each chapter, the reader will find a description of the proposed method as well as one or several examples of use with Python notebooks. These can be easily modified in order to be applied to specific applications. We also explain what the prerequisites are for using each technique, what the user will learn about them, and which tasks they are aimed at.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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