Demo Data from EDBT 2026 / reviewers in the wild / expert
EuJin Foo
dblp:359/0025
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › interpretability
explainable AI |
0.8 | 1 | 2024 | EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence · WSDM 2024 |
Machine learning › Trustworthy machine learning
interpretability |
0.8 | 1 | 2024 | EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial Intelligence · WSDM 2024 |
Human-AI interaction › explainable AI
explanation interfaces |
0.8 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | EvidenceQuest: An Interactive Evidence Discovery System for Explainable Artificial IntelligenceabstractExplainable 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 |
WSDM | 6 |
| 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 |
WISE | 6 |