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EuJin Foo

dblp:359/0025 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0009-0002-0451-2655ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Trustworthy machine learning · 100%
Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › interpretability
explainable AI
0.812024
EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence · WSDM 2024
Machine learning › Trustworthy machine learning
interpretability
0.812024
EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence · WSDM 2024
Human-AI interaction › explainable AI
explanation interfaces
0.812024
EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence · WSDM 2024

Methods — techniques the papers use, named apart from their topics

evidence-based design · 2.3
YearPublicationVenuePosition
2024 EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence
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.
Ambreen Hanif, Amin Beheshti, Xuyun Zhang, Steven Wood, Boualem Benatallah, EuJin Foo
WSDM6
2023 A Comprehensive Survey of Explainable Artificial Intelligence (XAI) Methods: Exploring Transparency and Interpretability
Ambreen Hanif, Amin Beheshti, Boualem Benatallah, Xuyun Zhang, Habiba, EuJin Foo, Nasrin Shabani, Maryam Shahabikargar
WISE6