Demo Data from SIGIR 2026 / reviewers in the wild / expert
Marie A. Jarrell
dblp:251/3541
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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.
| Human-computer interaction and pervasive computing
2 papers |
Games and playful interaction · 40% Collaborative and social computing · 27% Immersive interaction · 27% | |
| Artificial intelligence
1 paper |
Trustworthy machine learning · 100% |
Topics — the 6 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Immersive interaction
augmented reality |
0.7 | 1 | 2023 | Using Identification with AR Face Filters to Predict Explicit & Implicit Gender Bias · ISMAR 2023 |
Games and playful interaction › player-avatar relationship
character embodiment |
0.5 | 1 | 2021 | Using Intersectional Representation & Embodied Identification in Standard Video Game Play to Reduce Societal Biases · CHI 2021 |
Games and playful interaction › game playing
video game playing |
0.5 | 1 | 2021 | Using Intersectional Representation & Embodied Identification in Standard Video Game Play to Reduce Societal Biases · CHI 2021 |
Machine learning › Trustworthy machine learning
fairness |
0.2 | 1 | 2023 | Using Identification with AR Face Filters to Predict Explicit & Implicit Gender Bias · ISMAR 2023 |
Machine learning › Trustworthy machine learning › fairness
gender bias |
0.2 | 1 | 2023 | Using Identification with AR Face Filters to Predict Explicit & Implicit Gender Bias · ISMAR 2023 |
Human-AI interaction › responsible AI
bias reduction |
0.1 | 1 | 2021 | Using Intersectional Representation & Embodied Identification in Standard Video Game Play to Reduce Societal Biases · CHI 2021 |
Methods — techniques the papers use, named apart from their topics
implicit association test · 1.3experimental study · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Using Identification with AR Face Filters to Predict Explicit & Implicit Gender BiasabstractInternational audience Marie A. Jarrell, Etienne Peillard |
ISMAR | 1 |
| 2021 | Using Intersectional Representation & Embodied Identification in Standard Video Game Play to Reduce Societal BiasesabstractWhile virtual character embodiment has been studied as a mitigator of singular societal biases in fully immersive VR and empathy games, there have been no major studies on representation featuring standard game play or intersectional identities. In our study, participants played a short 2D video game with racial and gender character manipulations. They then rated a LinkedIn profile application to examine interactions of racial and gender biases. White male participants showed bias against black and female applicants, with the black female applicant experiencing both racial and gender bias. However, participants who embodied certain underrepresented characters in the game displayed reduced biases. Participants’ perceived identification with the characters moderated this effect. The study highlights a lack of homogeneity in the prevalence and potential reduction of different societal biases and incorporates intersectionality to illustrate how multiple parts of a player character’s identity can be used to combat biases. Marie A. Jarrell, Reza Ghaiumy Anaraky, Bart P. Knijnenburg, Erin Ash |
CHI | 1 |