EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence

被引：0

作者：

Hanif, Ambreen ^{[1
]}

Beheshti, Amin ^{[1
]}

Zhang, Xuyun ^{[1
]}

Wood, Steven ^{[2
]}

Benatallah, Boualem ^{[3
]}

Foo, Eu Jin ^{[1
]}

机构：

[1] Macquarie Univ, Sydney, NSW, Australia

[2] Prospa, Sydney, NSW, Australia

[3] Dublin City Univ, Dublin, Ireland

来源：

PROCEEDINGS OF THE 17TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, WSDM 2024 | 2024年

关键词：

Explainable Artificial Intelligence; Evidence; interactive dashboard; pipeline;

D O I：

10.1145/3616855.3635697

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Explainable Artificial Intelligence (XAI) aims to make artificial intelligence (AI) systems transparent and understandable to humans, providing clear explanations for the decisions made by AI models. This paper presents a novel pipeline and a digital dashboard that provides a user-friendly platform for interpreting the results of machine learning algorithms using XAI technology. The dashboard utilizes evidence-based design principles to deliver information clearly and concisely, enabling users to better understand the decisions made by their algorithms. We integrate XAI services into the dashboard to explain the algorithm's predictions, allowing users to understand howtheir models function and make informed decisions. We demonstrate a motivating scenario in banking and present how the proposed system enhances transparency and accountability and improves trust in the technology.

引用

页码：1058 / 1061

页数：4

共 23 条

[1] Explainability for artificial intelligence in healthcare: a multidisciplinary perspective [J].