Demo Data from SIGIR 2026 / reviewers in the wild / expert
Latisha Boor
dblp:339/2298
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-2501-3839ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 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.
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › human behavior modeling › social perception
stereotype content model |
0.7 | 1 | 2023 | Models of (Often) Ambivalent Robot Stereotypes: Content, Structure, and Predictors of Robots' Age and Gender Stereotypes · HRI 2023 |
Human-robot interaction › anthropomorphism
robot appearance |
0.2 | 1 | 2023 | Models of (Often) Ambivalent Robot Stereotypes: Content, Structure, and Predictors of Robots' Age and Gender Stereotypes · HRI 2023 |
Methods — techniques the papers use, named apart from their topics
online survey · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Models of (Often) Ambivalent Robot Stereotypes: Content, Structure, and Predictors of Robots' Age and Gender StereotypesabstractThis study focused on investigating the content, structure, and predictors of robots' stereotypes. We involved 120 participants in an online study and asked them to rate 80 robots on communion, agency, suitability for female and suitability for male tasks. In line with the stereotype content model, we discovered that robots' stereotypes are described by two dimensions, communion and agency, which combine to form univalent (e.g., low communion/low agency), as well as ambivalent clusters (e.g., low communion/high agency). Moreover, we found out that a robot's stereotypical appearance has a role in activating stereotypes. Indeed, in our study, female robots featuring appearance cues socio-culturally associated with femininity (e.g., eyelashes or apparel) were perceived as more communal, and juvenile robots featuring appearance cues tapping into the baby schema (e.g., cartoony eyes) were perceived as more communal, less agentic, and less suited to perform tasks. Given the renowned relationship between stereotyping, prejudice and discrimination, the causal link between appearance and stereotyping we establish in this paper can help HRI researchers disentangle the relation between robots' design and people's behavioral tendencies towards them, including proneness to harm. Giulia Perugia, Latisha Boor, Laura van der Bij, Okke Rikmenspoel, Robin Foppen, Stefano Guidi |
HRI | 2 |