A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots

Murtarelli, Grazia; Gregory, Anne; Romenti, Stefania

doi:10.1016/j.jbusres.2020.09.018

Cited by 108 publications

(65 citation statements)

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“…language style, human-associated identity) to humanize chatbots have been conducted. Prior findings suggest that anthropomorphism is positively related to trust and adoption intention, and such positive perception of chatbots may improve overall perception of and emotional connection with the service provider providing these agents (Araujo, 2018; Murtarelli et al , 2020; Sheehan et al , 2020). Another research line focuses on the functionality of chatbots.…”

Section: Literature Reviewmentioning

confidence: 98%

“…Such reliance on TAM and its extended model reaffirms the limitations of our current understanding of chatbot use. We argue that the value of chatbots cannot be fully understood through the technological and anthropomorphic lenses alone (Murtarelli et al , 2020). Other aspects, such as a chatbot’s overall problem-solving capabilities, its relationship with the user, and factors concerning the service context should also be considered (Følstad et al , 2018; Murtarelli et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

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A comparison between chatbot and human service: customer perception and reuse intention

Lei

Shen

2021

IJCHM

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PurposeChatbot users’ communication experience with disembodied conversational agents was compared with instant messaging (IM) users’ communication experience with human conversational agents. The purpose of this paper is to identify what affects users’ intention to reuse and whether they perceive any difference between the two. Design/methodology/approachA conceptual model was developed based on computer-mediated communication (CMC) and interpersonal communication theories. Data were collected online from four different continents (North America, Europe, Asia and Australia). Partial least squares structural equation modeling was applied to examine the research model. FindingsThe findings mainly reveal that media richness and social presence positively influence trust and reuse intention through task attraction and social attraction; IM users reported significantly higher scores in terms of communication experience, perceived attractiveness of the conversational agent, and trust than chatbot users; users’ trust in the conversational agents is mainly determined by perceived task attraction. Research limitations/implicationsCustomers’ evaluation of the communication environment is positively related to their perceived competence of the conversational agent which ultimately affect their intention to reuse chatbot/IM. The findings reveal determinants of chatbot/IM adoption which have rarely been mentioned by previous work. Practical implicationsPractitioners should note that consumers in general still prefer to interact with human conversational agents. Practitioners should contemplate how to combine chatbot and human resources effectively to deliver the best customer service. Originality/valueThis study goes beyond the Computer as Social Actor paradigm and Technology Acceptance Model to understand chatbot and IM adoption. It is among one of the first studies that compare chatbot and IM use experience in the tourism and hospitality literature.

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Section: Literature Reviewmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

A comparison between chatbot and human service: customer perception and reuse intention

Lei

Shen

2021

IJCHM

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“…As the various forms of chatbots increasingly integrate the design of AI mechanisms (such as game theory, data mining and optimisation techniques), they comply with these networks' rules and dynamics. This form can be characterised by real multi-actor-based conversations that require technical resources, specialisedknowledge and communication skills to maintain online interactions [10]. Summarized by the acronym AI, this is a science that focuses on handling the production of human knowledge, and can offer to the machine the ability to imitate human reasoning and intelligence [11].…”

Section: Methodsmentioning

confidence: 99%

Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme

Anki

Bustamam²

2021

IJEECS

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Python programme contains a question and answer system that derived from data sets that have used and implemented the chatbot in this modern era. where the data collected is in the form of corpuses containing extensive metadata-rich fictional conversations derived from extracted film scripts, commonly called cornell movie dialogue corpus. The various models have been used chatbots in python programmes, and LSTM and BiLSTM models were specifically used in this study. Where the form of accuracy will be reported as a result of the implementation of LSTM and BiLSTM models in the chatbot programme. The programme performance will be influenced by the data from the model selection, because the level of accuracy is determined by the target programme being taken. So this is the main factor that determines which model to choose. Based on considerations required for choosing the programme model, in the end the LSTM and the BiLSTM models are chosen and will be applied to the programme. Based on the LSTM and BiLSTM chatbot programmes that have been tested, it can be concluded that the best parameters come from a pair of BiLSTM chatbots using the BiLTSM model with an average accuracy value of 0.995217.

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“…However, it is notable that other researchers in e-learning appear more interested in the ethical practices of the students, such as cheating and fraud (e.g., Gearhart 2012). Finally, researchers and developers of intelligent agents such as chatbots are also increasingly focusing on the ethical issues raised by their work (e.g., Murtarelli et al 2020;Richards and Dignum 2019). Most recently, the World Economic Forum has launched 'RESET', a framework and set of principles for governing responsible use of conversational AI (WEF 2020)which, although focused on healthcare, have self-evident potential for the ethics of intelligent agents in education.…”

Section: The Ethics Of Other Related Research Areasmentioning

confidence: 99%

Ethics of AI in Education: Towards a Community-Wide Framework

Holmes¹,

Porayska-Pomsta²,

Holstein

et al. 2021

Int J Artif Intell Educ

299

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While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.

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A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots

Cited by 108 publications

References 50 publications

A comparison between chatbot and human service: customer perception and reuse intention

A comparison between chatbot and human service: customer perception and reuse intention

Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme

Ethics of AI in Education: Towards a Community-Wide Framework

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