Group-based Emotions in Teams of Humans and Robots

Correia, Filipa; Mascarenhas, Samuel; Prada, Rui; Melo, Francisco S.; Paiva, Ana

doi:10.1145/3171221.3171252

Cited by 69 publications

(27 citation statements)

References 35 publications

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“…Group interaction with robots often takes the form of intergroup behavior similar to social psychology ( Fraune et al, 2015a , b , 2017a ; Leite et al, 2015 ). Participants categorize robots as ingroup or outgroup members based on perceived robot gender ( Eyssel et al, 2012 ), nationality ( Kuchenbrandt et al, 2013 ; Correia et al, 2016 ), helpfulness ( Bartneck et al, 2007 ), and robot use of group-based emotions (e.g., pride in the group; Correia et al, 2018 ).…”

Section: Related Workmentioning

confidence: 99%

Our Robots, Our Team: Robot Anthropomorphism Moderates Group Effects in Human–Robot Teams

Fraune

2020

Front. Psychol.

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Past research indicates that people favor, and behave more morally toward, human ingroup than outgroup members. People showed a similar pattern for responses toward robots. However, participants favored ingroup humans more than ingroup robots. In this study, I examine if robot anthropomorphism can decrease differences between humans and robots on ingroup favoritism. This paper presents a 2 × 2 × 2 mixed-design experimental study with participants (N = 81) competing on teams of humans and robots. I examined how people morally behaved toward and perceived players depending on players' Group Membership (ingroup, outgroup), Agent Type (human, robot), and Robot Anthropomorphism (anthropomorphic, mechanomorphic). Results replicated prior findings that participants favored the ingroup over the outgroup and humans over robots-to the extent that they favored ingroup robots over outgroup humans. This paper also includes novel results indicating that patterns of responses toward humans were more closely mirrored by anthropomorphic than mechanomorphic robots.

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Section: Related Workmentioning

confidence: 99%

Our Robots, Our Team: Robot Anthropomorphism Moderates Group Effects in Human–Robot Teams

Fraune

2020

Front. Psychol.

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“…In AI, a multiplayer version of the UG can further constitute a convenient interaction model to test the emergence of fairness in groups composed by humans and agents (Correia et al 2018), which can be of natural interest for areas such as multiagent resource allocation (Chevaleyre et al 2006) or multilateral automated bargaining (Jennings et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Evolution of Collective Fairness in Hybrid Populations of Humans and Agents

Santos

Pacheco

Paiva

et al. 2019

AAAI

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Fairness plays a fundamental role in decision-making, which is evidenced by the high incidence of human behaviors that result in egalitarian outcomes. This is often shown in the context of dyadic interactions, resorting to the Ultimatum Game. The peculiarities of group interactions – and the corresponding effect in eliciting fair actions – remain, however, astray. Focusing on groups suggests several questions related with the effect of group size, group decision rules and the interrelation of human and agents’ behaviors in hybrid groups. To address these topics, here we test a Multiplayer version of the Ultimatum Game (MUG): proposals are made to groups of Responders that, collectively, accept or reject them. Firstly, we run an online experiment to evaluate how humans react to different group decision rules. We observe that people become increasingly fair if groups adopt stricter decision rules, i.e., if more individuals are required to accept a proposal for it to be accepted by the group. Secondly, we propose a new analytical model to shed light on how such behaviors may have evolved. Thirdly, we adapt our model to include agents with fixed behaviors. We show that including hardcoded Pro-social agents favors the evolutionary stability of fair states, even for soft group decision rules. This suggests that judiciously introducing agents with particular behaviors in a population may leverage long-term social benefits.

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“…Furthermore, in settings with many human participants, robots can guide interactions between humans by adjusting their speech, focus, and actions (9,(22)(23)(24)(25). This effect is especially pronounced when the content of robot speech is based on group emotions, which can lead to a more positive view of the robot (26). As robots increasingly populate our homes and workplaces, the ubiquitous presence of such groups composed of humans and machines could alter how humans relate to both robots and each other (27)(28)(29).…”

mentioning

confidence: 99%

Vulnerable robots positively shape human conversational dynamics in a human–robot team

Traeger

Sebo

Jung

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Social robots are becoming increasingly influential in shaping the behavior of humans with whom they interact. Here, we examine how the actions of a social robot can influence human-to-human communication, and not just robot–human communication, using groups of three humans and one robot playing 30 rounds of a collaborative game (n= 51 groups). We find that people in groups with a robot making vulnerable statements converse substantially more with each other, distribute their conversation somewhat more equally, and perceive their groups more positively compared to control groups with a robot that either makes neutral statements or no statements at the end of each round. Shifts in robot speech have the power not only to affect how people interact with robots, but also how people interact with each other, offering the prospect for modifying social interactions via the introduction of artificial agents into hybrid systems of humans and machines.

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Group-based Emotions in Teams of Humans and Robots

Cited by 69 publications

References 35 publications

Our Robots, Our Team: Robot Anthropomorphism Moderates Group Effects in Human–Robot Teams

Our Robots, Our Team: Robot Anthropomorphism Moderates Group Effects in Human–Robot Teams

Evolution of Collective Fairness in Hybrid Populations of Humans and Agents

Vulnerable robots positively shape human conversational dynamics in a human–robot team

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