Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mary Gick
dblp:25/3486
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author
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 · 77% Knowledge representation and reasoning · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › interpretability
explanation evaluation |
0.0 | 1 | 1991 | The Importance of Causal Structure and Facts in Evaluating Explanations · ML 1991 |
Machine learning › Trustworthy machine learning
interpretability |
0.0 | 1 | 1991 | The Importance of Causal Structure and Facts in Evaluating Explanations · ML 1991 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
causal reasoning |
0.0 | 1 | 1991 | The Importance of Causal Structure and Facts in Evaluating Explanations · ML 1991 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › causality
causal structure |
0.0 | 1 | 1991 | The Importance of Causal Structure and Facts in Evaluating Explanations · ML 1991 |
Methods — techniques the papers use, named apart from their topics
fact-based evaluation · 0.0causal structure analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | The Importance of Causal Structure and Facts in Evaluating Explanations
Mary Gick, Stan Matwin |
ML | 1 |