I'm Sorry, Dave, I'm Afraid I Can't Do That

Zamora, Jennifer

doi:10.1145/3125739.3125766

Cited by 122 publications

(30 citation statements)

References 9 publications

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“…Our analysis also supports the findings from a qualitative study exploring user expectations of chatbots in terms of their understanding and preferences. 32 Users are generally unclear about what chatbots can do, although they foresee this technology as improving their experience by providing immediate access to relevant and valuable information. It was also shown that users saw the lack of judgement as a unique aspect of this technology, although it was noted that building rapport with a chatbot would require trust and meaningful interactions.…”

Section: Discussionmentioning

confidence: 99%

Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study

et al. 2019

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Background Artificial intelligence (AI) is increasingly being used in healthcare. Here, AI-based chatbot systems can act as automated conversational agents, capable of promoting health, providing education, and potentially prompting behaviour change. Exploring the motivation to use health chatbots is required to predict uptake; however, few studies to date have explored their acceptability. This research aimed to explore participants’ willingness to engage with AI-led health chatbots. Methods The study incorporated semi-structured interviews (N-29) which informed the development of an online survey (N-216) advertised via social media. Interviews were recorded, transcribed verbatim and analysed thematically. A survey of 24 items explored demographic and attitudinal variables, including acceptability and perceived utility. The quantitative data were analysed using binary regressions with a single categorical predictor. Results Three broad themes: ‘Understanding of chatbots’, ‘AI hesitancy’ and ‘Motivations for health chatbots’ were identified, outlining concerns about accuracy, cyber-security, and the inability of AI-led services to empathise. The survey showed moderate acceptability (67%), correlated negatively with perceived poorer IT skills OR = 0.32 [CI 95% :0.13–0.78] and dislike for talking to computers OR = 0.77 [CI 95% :0.60–0.99] as well as positively correlated with perceived utility OR = 5.10 [CI 95% :3.08–8.43], positive attitude OR = 2.71 [CI 95% :1.77–4.16] and perceived trustworthiness OR = 1.92 [CI 95% :1.13–3.25]. Conclusion Most internet users would be receptive to using health chatbots, although hesitancy regarding this technology is likely to compromise engagement. Intervention designers focusing on AI-led health chatbots need to employ user-centred and theory-based approaches addressing patients’ concerns and optimising user experience in order to achieve the best uptake and utilisation. Patients’ perspectives, motivation and capabilities need to be taken into account when developing and assessing the effectiveness of health chatbots.

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Section: Discussionmentioning

confidence: 99%

Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study

et al. 2019

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“…These beliefs help encourage people to utilize chatbots. As such, participants commonly cite the agents' ability to talk about embarrassing topics and listen without being judgmental (Zamora, 2017).…”

Section: Agents For Mental Healthmentioning

confidence: 99%

“…The user interface guided users step-by-step through the chatbot conversation (Zamora, 2017). Following the use of text-and menu-based interfaces on standard social media platforms (e.g., Facebook, 2019), participants conversed with the chatbot mainly via multiple-choice menus (Bickmore and Schulman, 2007;Nguyen and Masthoff, 2009), which were updated in succession depending on the current conversation topic , and only occasionally using the text interface.…”

Section: Experimental Conditionsmentioning

confidence: 99%

Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood

2020

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From past research it is well known that social exclusion has detrimental consequences for mental health. To deal with these adverse effects, socially excluded individuals frequently turn to other humans for emotional support. While chatbots can elicit social and emotional responses on the part of the human interlocutor, their effectiveness in the context of social exclusion has not been investigated. In the present study, we examined whether an empathic chatbot can serve as a buffer against the adverse effects of social ostracism. After experiencing exclusion on social media, participants were randomly assigned to either talk with an empathetic chatbot about it (e.g., "I'm sorry that this happened to you") or a control condition where their responses were merely acknowledged (e.g., "Thank you for your feedback"). Replicating previous research, results revealed that experiences of social exclusion dampened the mood of participants. Interacting with an empathetic chatbot, however, appeared to have a mitigating impact. In particular, participants in the chatbot intervention condition reported higher mood than those in the control condition. Theoretical, methodological, and practical implications, as well as directions for future research are discussed.

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“…For first-time chatbot users, Jain et al [4] observed a tendency to gender chatbots and react with annoyance when presented with scripted replies. Previous qualitative studies [8] have highlighted users' desire to further express their emotions in a medium perceived as non-judgmental, such as with chatbots. Similarly, Portela and Granell-Canut emphasized the need for a balanced approach to human-like behavior in chatbots in order to minimize confusion [7].…”

Section: Related Workmentioning

confidence: 99%

Chats with Bots

Muresan

Pohl

2019

Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

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Advances in AI are paving the way towards more natural interactions, blurring the line between bot and human. We present findings from a two-week diary study exploring users' interactions with the chatbot Replika. In particular, we focus on how users anthropomorphize chatbots and how this influences their engagement. We find that failing to adhere to social norms and glaring signs of humanity leads to decreased engagement unless balanced appropriately. CCS CONCEPTS • Information systems → Chat; • Human-centered computing → Natural language interfaces.

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I'm Sorry, Dave, I'm Afraid I Can't Do That

Cited by 122 publications

References 9 publications

Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study

Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study

Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood

Chats with Bots

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