EvidenceQuest: an interactive evidence discovery system for explainable artificial intelligence

Ambreen Hanif, Amin Beheshti, Xuyun Zhang, Steven Wood, Boualem Benatallah, Eu Jin Foo

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

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 how their 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.
Original languageEnglish
Title of host publicationWSDM '24
Subtitle of host publicationproceedings of the 17th ACM International Conference on Web Search and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages1058-1061
Number of pages4
ISBN (Electronic)9798400703713
DOIs
Publication statusPublished - 2024
EventACM International Conference on Web Search and Data Mining (17th : 2024) - Merida, Mexico
Duration: 4 Mar 20248 Mar 2024
Conference number: 17th

Conference

ConferenceACM International Conference on Web Search and Data Mining (17th : 2024)
Abbreviated titleWSDM '24
Country/TerritoryMexico
CityMerida
Period4/03/248/03/24

Keywords

  • Explainable Artificial Intelligence
  • Evidence
  • interactive dashboard
  • pipeline

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