How should my chatbot interact? A survey on human-chatbot interaction design

Chaves, Ana Paula; Gerosa, Marco Aurelio

doi:10.48550/arxiv.1904.02743

Cited by 9 publications

(15 citation statements)

References 54 publications

(298 reference statements)

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“…We sought a conversational AI agent whose actual performance was strong enough for our result to generalize as the underlying AI models improve. So, following a pattern in prior work [12,34,71,74], we adopt a Wizard-of-Oz study paradigm.…”

Section: Wizard-of-oz Conversational Agentmentioning

confidence: 99%

“…We perform an experiment (N = 260) that manipulates the metaphor associated with an AI agent and measures how it invokes expectations of competence and warmth and how those two dimensions affect ratings of usability, intention to adopt, and desire to cooperate. We draw on an established method from prior experiments [12,34,71,74] to instantiate the agent itself as a remote Wizard-of-Oz who is blind to the condition and randomized across conditions for each participant. Participants are first exposed to the agent's metaphor, then converse with the agent to complete a travel planning task [4].…”

Section: Introductionmentioning

confidence: 99%

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Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

Khadpe

Krishna

et al. 2020

Proc. ACM Hum.-Comput. Interact.

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With the emergence of conversational artificial intelligence (AI) agents, it is important to understand the mechanisms that influence users' experiences of these agents. In this paper, we study one of the most common tools in the designer's toolkit: conceptual metaphors. Metaphors can present an agent as akin to a wry teenager, a toddler, or an experienced butler. How might a choice of metaphor influence our experience of the AI agent? Sampling a set of metaphors along the dimensions of warmth and competence-defined by psychological theories as the primary axes of variation for human social perception-we perform a study (N = 260) where we manipulate the metaphor, but not the behavior, of a Wizard-of-Oz conversational agent. Following the experience, participants are surveyed about their intention to use the agent, their desire to cooperate with the agent, and the agent's usability. Contrary to the current tendency of designers to use high competence metaphors to describe AI products, we find that metaphors that signal low competence lead to better evaluations of the agent than metaphors that signal high competence. This effect persists despite both high and low competence agents featuring identical, human-level performance and the wizards being blind to condition. A second study confirms that intention to adopt decreases rapidly as competence projected by the metaphor increases. In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth. These results suggest that projecting competence may help attract new users, but those users may discard the agent unless it can quickly correct with a lower competence metaphor. We close with a retrospective analysis that finds similar patterns between metaphors and user attitudes towards past conversational agents such as Xiaoice, Replika, Woebot, Mitsuku, and Tay.CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing; Empirical studies in HCI; Human computer interaction (HCI).

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Section: Wizard-of-oz Conversational Agentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

Khadpe

Krishna

et al. 2020

Proc. ACM Hum.-Comput. Interact.

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“…We generally train on a noisy corpus and do not train/enforce it to be decent, which may lead to indecent responses by the agent. Several instances in the past have shown indecent behavior by the AI systems [12,17,26] and many of such systems have been decommissioned due to their indecent responses. Therefore, we believe that in order to build a trustworthy AI system, it is very important to have ensure that the system is decent.…”

Section: Introductionmentioning

confidence: 99%

Trustworthy AI

Singh

Vatsa

Ratha

2021

Proceedings of the 3rd ACM India Joint International Conference on Data Science &Amp; Management of Data (8th ACM IKDD CODS &Am

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Modern AI systems are reaping the advantage of novel learning methods. With their increasing usage, we are realizing the limitations and shortfalls of these systems. Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, high opacity in terms of revealing the lineage of the system, how they were trained and tested, and under which parameters and conditions they can reliably guarantee a certain level of performance, are some of the most prominent limitations. Ensuring the privacy and security of the data, assigning appropriate credits to data sources, and delivering decent outputs are also required features of an AI system. We propose the tutorial on "Trustworthy AI" to address six critical issues

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“…In each context, personal information is increasingly collected and processed in order to provide more effective and personalized services. Personalization allows a chatbot to be aware of situations and to dynamically adapt its interaction to be er suit user needs [1,2]. Although deeper disclosures of personal information can increase the effective use of these agents, an intriguing-and thus far unaddressedtension arises between the need for sharing such information (in one-off situations, or for personalized interactions) and privacy (a primary goal of regulations such as the GDPR).…”

Section: Introductionmentioning

confidence: 99%

Is your chatbot GDPR compliant? Open issues in agent design

Saglam,

Nurse

2020

Preprint

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Conversational agents open the world to new opportunities for human interaction and ubiquitous engagement. As their conversational abilities and knowledge has improved, these agents have begun to have access to an increasing variety of personally identifiable information and intimate details on their user base. is access raises crucial questions in light of regulations as robust as the General Data Protection Regulation (GDPR). is paper explores some of these questions, with the aim of defining relevant open issues in conversational agent design. We hope that this work can provoke further research into building agents that are effective at user interaction, but also respectful of regulations and user privacy.

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How should my chatbot interact? A survey on human-chatbot interaction design

Cited by 9 publications

References 54 publications

Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

Trustworthy AI

Is your chatbot GDPR compliant? Open issues in agent design

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