EDBT 2026 Demo / reviewers in the wild / expert
Laura M. Vardoulakis
dblp:402/9933
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0009-9818-9334ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 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.
| Human-computer interaction and pervasive computing
2 papers |
Human-AI interaction · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Medical and health informatics · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction › human-centered AI
participatory AI |
0.9 | 1 | 2025 | Participatory AI Considerations for Advancing Racial Health Equity · CHI 2025 |
Methods — techniques the papers use, named apart from their topics
qualitative workshops · 2.0participatory design · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Promise or Peril? Exploring Black Adults' Perspectives on the Use of Artificial Intelligence in Health ContextsabstractAs artificial intelligence (AI) is rapidly integrated into healthcare, ensuring that this innovation helps to combat health inequities requires engaging marginalized communities in health AI futuring. However, little research has examined Black populations’ perspectives on the use of AI in health contexts, despite the widespread health inequities they experience–inequities that are already perpetuated by AI. Addressing this research gap, through qualitative workshops with 18 Black adults, we characterize participants’ cautious optimism for health AI addressing structural well-being barriers (e.g., by providing second opinions that introduce fairness into an unjust healthcare system), and their concerns that AI will worsen health inequities (e.g., through health AI biases they deemed inevitable and the problematic reality of having to trust healthcare providers to use AI equitably). We advance health AI research by articulating previously-unreported health AI perspectives from a population experiencing significant health inequities, and presenting key considerations for future work. Andrea G. Parker, Laura M. Vardoulakis, Christina N. Harrington |
CHI | 2 |
| 2025 | Participatory AI Considerations for Advancing Racial Health Equity
Andrea G. Parker, Laura M. Vardoulakis, Jatin Alla, Christina N. Harrington |
CHI | 2 |