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.
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CITATION STYLE
Hanif, A., Beheshti, A., Zhang, X., Wood, S., Benatallah, B., & Foo, E. J. (2024). EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence. In WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining (pp. 1058–1061). Association for Computing Machinery, Inc. https://doi.org/10.1145/3616855.3635697
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