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Nevo Heimann Saadon

dblp:374/8796 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0008-6573-9806ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 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
1 paper
Human-robot interaction · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › trust in robots
robot deception
0.912025
Scammed By Robots: Can Deception by Two Simple Non-Humanoid Robots Shape General Attitudes toward Robots? · HRI 2025
Human-robot interaction
trust in robots
0.912025
Scammed By Robots: Can Deception by Two Simple Non-Humanoid Robots Shape General Attitudes toward Robots? · HRI 2025
Human-robot interaction
attitudes toward robots
0.312025
Scammed By Robots: Can Deception by Two Simple Non-Humanoid Robots Shape General Attitudes toward Robots? · HRI 2025
Human-robot interaction
multi-robot systems
0.312025
Scammed By Robots: Can Deception by Two Simple Non-Humanoid Robots Shape General Attitudes toward Robots? · HRI 2025

Methods — techniques the papers use, named apart from their topics

gambling game experiment · 0.9
YearPublicationVenuePosition
2025 Scammed By Robots: Can Deception by Two Simple Non-Humanoid Robots Shape General Attitudes toward Robots?
abstract
As multi-robot environments become more common, it is important to assess the robots' impact on the humans interacting with them and its magnitude. We investigated whether an interaction with two simple non-humanoid robots can be perceived as an intentional collusion aimed to deceive the person interacting with them. We evaluated if such a negative interaction would result in shaping people's perception of the robots in the interaction and extend to shaping their attitudes towards robots in general. In the study, participants played a gambling game with two simple non-humanoid robots: a “Dealer” responsible for card shuffling and an “Advisor” providing recommendations. We tested participants' experience when the “Advisor” robot gave a false recommendation in the final round, causing them to lose all their winnings. Our findings indicated that this single instance of perceived deception led to a negative perception of the “Advisor” robot, and also negatively influenced people's attitudes toward robots in general, at least temporarily. This study highlights the importance of carefully designing interactions in multi-robot environments, even when the interaction involves very simple robots. Careful consideration of ingroup-outgroup effects and verifying that the robots' collaboration is not interpreted as intentional deception is essential for avoiding powerful negative effects that reach beyond the specific interaction with the robots.
Nevo Heimann Saadon, Benny Megidish, Hadas Erel
HRI1