Bias in, Bias Out – the Similarity-Attraction Effect Between Chatbot Designers and Users

Zabel, Sarah; Otto, Siegmar

doi:10.1007/978-3-030-78468-3_13

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…However, our results on the influence of overall moral intensity further highlight the need for reconsidering predictors of technology use when it comes to technologies for the common good. While user experience certainly is a predictor of technology use ( Zabel & Otto, 2021 ), focusing on the objectives of the particular technology, including moral issues, is at least as important.…”

Section: Discussionmentioning

confidence: 99%

Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing

Zabel

Schlaile

Otto

2023

Computers in Human Behavior

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Section: Discussionmentioning

confidence: 99%

Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing

Zabel

Schlaile

Otto

2023

Computers in Human Behavior

Self Cite

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“…Schools and educators should be aware of any potential risks. They are associated with using chatbots, such as the potential for bias in the programming of the chatbot (Zabel & Otto, 2021). Furthermore, using chatbots in a flipped learning context should be done with appropriate safeguards, such as an opt-out option for students who do not wish to participate (Valério et al, 2020).…”

Section: Step Descriptionmentioning

confidence: 99%

Chatbots and Flipped Learning: Enhancing Student Engagement and Learning Outcomes through Personalised Support and Collaboration

Baskara

2023

IJORER Int. J. Recent Educ. Res.

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Objective: This paper explored the relationship between chatbots and flipped learning in an educational setting. It also identifies the benefits and drawbacks of using chatbots in a flipped learning context and the ethical and privacy concerns related to their use. Method: The study utilized a theoretical analysis approach, which included a comprehensive review of relevant literature from Scopus and World of Science databases. The data collected from the literature review was analyzed using a qualitative approach. Results: The study found that chatbots can potentially enhance student engagement and learning outcomes in a flipped learning context by providing personalized support, facilitating group discussions and collaborations, providing feedback and assessment on student work, supporting self-directed learning, and enhancing student engagement and motivation. However, using chatbots in a flipped learning context also raises ethical and privacy concerns, including data privacy, data security, and student anonymity. Novelty: Contributes to the existing research on using chatbots in education by providing insights into the potential benefits and drawbacks of using chatbots in a flipped learning context. The study highlights the importance of considering the ethical and privacy concerns and the future potential of chatbots in a flipped learning context and proposes future research directions.

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“…age, gender, and race) of robotic programmers and designers together with biased or unrepresentative training data feeding into algorithmic bias impact service outcomes (Ukanwa and Rust, 2021). For example, Zabel and Otto (2021) have shown gender differences in programming affect user satisfaction with a chatbot. We recommend scholars to investigate if the individual characteristics of robot designers might affect robot programming and with it, role enactment and reactions to consumers.…”

Section: P3mentioning

confidence: 99%

Robotic role theory: an integrative review of human–robot service interaction to advance role theory in the age of social robots

Blaurock

Čaić

Okan

et al. 2022

JOSM

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PurposeSocial robots increasingly adopt service roles in the marketplace. While service research is beginning to unravel the implications for theory and practice, other scientific disciplines have amassed a wealth of empirical data of robots assuming such service roles. The purpose of this paper is to synthesize these findings from a role theory perspective with the aim of advancing role theory for human–robot service interaction (HRSI).Design/methodology/approachA systematic review of more than 10,000 articles revealed 149 empirical HRSI-related papers across scientific disciplines. The respective articles are analyzed employing qualitative content analysis through the lens of role theory.FindingsThis review develops an organizing structure of the HRSI literature across disciplines, delineates implications for role theory development in the age of social robots, and advances robotic role theory by providing an overarching framework and corresponding propositions. Finally, this review introduces avenues for future research.Originality/valueThis study pioneers a comprehensive review of empirical HRSI literature across disciplines adopting the lens of role theory. The study structures the body of HRSI literature, adapts traditional and derives novel propositions for role theory (i.e. robotic role theory), and delineates promising future research opportunities.

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Bias in, Bias Out – the Similarity-Attraction Effect Between Chatbot Designers and Users

Cited by 8 publications

References 30 publications

Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing

Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing

Chatbots and Flipped Learning: Enhancing Student Engagement and Learning Outcomes through Personalised Support and Collaboration

Robotic role theory: an integrative review of human–robot service interaction to advance role theory in the age of social robots

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