Boosting Robot Credibility and Challenging Gender Norms in Responding to Abusive Behaviour

Winkle, Katie; Melsión, Gaspar Isaac; Leite, Iolanda

doi:10.1145/3434074.3446910

Cited by 61 publications

(39 citation statements)

References 21 publications

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“…As such, feminist epistemology rejects the notion of an “objective” science, but challenges the values, norms and assumptions that inform our research by critical reflections and questioning the research questions, questioning the language we use in the scientific processes, or by locating the researcher in the research process ( Wigginton and Lafrance, 2019 ). In HRI, too, there are already first articles grounded in feminist epistemology that could be further explored in future work ( Weber, 2005 ; Winkle et al, 2021 ). Another example is critical psychology that also recognizes that theories, methods, and practices are determined by socio-historical contexts.…”

Section: Toward More Reliable Hri Researchmentioning

confidence: 99%

Crisis Ahead? Why Human-Robot Interaction User Studies May Have Replicability Problems and Directions for Improvement

2022

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There is a confidence crisis in many scientific disciplines, in particular disciplines researching human behavior, as many effects of original experiments have not been replicated successfully in large-scale replication studies. While human-robot interaction (HRI) is an interdisciplinary research field, the study of human behavior, cognition and emotion in HRI plays also a vital part. Are HRI user studies facing the same problems as other fields and if so, what can be done to overcome them? In this article, we first give a short overview of the replicability crisis in behavioral sciences and its causes. In a second step, we estimate the replicability of HRI user studies mainly 1) by structural comparison of HRI research processes and practices with those of other disciplines with replicability issues, 2) by systematically reviewing meta-analyses of HRI user studies to identify parameters that are known to affect replicability, and 3) by summarizing first replication studies in HRI as direct evidence. Our findings suggest that HRI user studies often exhibit the same problems that caused the replicability crisis in many behavioral sciences, such as small sample sizes, lack of theory, or missing information in reported data. In order to improve the stability of future HRI research, we propose some statistical, methodological and social reforms. This article aims to provide a basis for further discussion and a potential outline for improvements in the field.

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Section: Toward More Reliable Hri Researchmentioning

confidence: 99%

Crisis Ahead? Why Human-Robot Interaction User Studies May Have Replicability Problems and Directions for Improvement

2022

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“…In a real-life environment, how should a female robot react to people issuing annoyance for their lack of compliance or consent? Should they maintain a jokey vibe of servitude as VPAs originally did [91] or react resolutely as in Winkle et al [92]? We consider Winkle et al's work [92] a valid and viable option.…”

Section: Discussion On Effects Of Robot's Genderedness On Perceptions...mentioning

confidence: 99%

Robot's Gendering Trouble: A Scoping Review of Gendering Humanoid Robots and its Effects on HRI

Perugia¹,

Lisy²

2022

Preprint

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The discussion around the problematic practice of gendering humanoid robots has risen to the foreground in the last few years. To lay the basis for a thorough understanding of how robot's "gender" has been understood within the Human-Robot Interaction (HRI) community -i.e., how it has been manipulated, in which contexts, and which effects it has yield on people's perceptions and interactions with robots -we performed a scoping review of the literature. We identified 553 papers relevant for our review retrieved from 5 different databases. The final sample of reviewed papers included 35 papers written between 2005 and 2021, which involved a total of 3902 participants. In this article, we thoroughly summarize these papers by reporting information about their objectives and assumptions on gender (i.e., definitions and reasons to manipulate gender), their manipulation of robot's "gender" (i.e., gender cues and manipulation checks), their experimental designs (e.g., demographics of participants, employed robots), and their results (i.e., main and interaction effects). The review reveals that robot's "gender" does not affect crucial constructs for the HRI, such as likability and acceptance, but rather bears its strongest effect on stereotyping. We leverage our different epistemological backgrounds in Social Robotics and Gender Studies to provide a comprehensive interdisciplinary perspective on the results of the review and suggest ways to move forward in the field of HRI.

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“…In a similar direction, the book Data Feminism is presented to point out data ethics considering ideas of intersectional feminism [15]. More closely, in the Human-Robot Interaction (HRI) field, feminist considerations have been proposed in the design of robots [16], [17]. Additionally, numerous works address questions regarding the genre-related attributes of robots and how robots are perceived according to those attributes [18], [19].…”

Section: Precedents Of Feminism As An Agenda For Change In Ai and Rob...mentioning

confidence: 99%

Feminist Perspective on Robot Learning Processes

Hurtado¹,

Mejia²

2022

Preprint

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As different research works report and daily life experiences confirm, learning models can result in biased outcomes. The biased learned models usually replicate historical discrimination in society and typically negatively affect the less represented identities. Robots are equipped with these models that allow them to operate, performing tasks more complex every day. The learning process consists of different stages depending on human judgments. Moreover, the resulting learned models for robot decisions rely on recorded labeled data or demonstrations. Therefore, the robot learning process is susceptible to bias linked to human behavior in society. This imposes a potential danger, especially when robots operate around humans and the learning process can reflect the social unfairness present today. Different feminist proposals study social inequality and provide essential perspectives towards removing bias in various fields. What is more, feminism allowed and still allows to reconfigure numerous social dynamics and stereotypes advocating for equality across people through their diversity. Consequently, we provide a feminist perspective on the robot learning process in this work. We base our discussion on intersectional feminism, community feminism, decolonial feminism, and pedagogy perspectives, and we frame our work in a feminist robotics approach. In this paper, we present an initial discussion to emphasize the relevance of feminist perspectives to explore, foresee, en eventually correct the biased robot decisions.

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Boosting Robot Credibility and Challenging Gender Norms in Responding to Abusive Behaviour

Cited by 61 publications

References 21 publications

Crisis Ahead? Why Human-Robot Interaction User Studies May Have Replicability Problems and Directions for Improvement

Crisis Ahead? Why Human-Robot Interaction User Studies May Have Replicability Problems and Directions for Improvement

Robot's Gendering Trouble: A Scoping Review of Gendering Humanoid Robots and its Effects on HRI

Feminist Perspective on Robot Learning Processes

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