Chatbots, Humbots, and the Quest for Artificial General Intelligence

Grudin, Jonathan; Jacques, Richard

doi:10.1145/3290605.3300439

Cited by 192 publications

(138 citation statements)

References 17 publications

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“…In the area of online communities, for example, Geiger and Halfaker examined such partnerships of bots and editors in Wikipedia but also the challenges when mixing human and algorithmic governance (e.g., [21,22,27]). These considerations might lead to several interesting questions for the CSCW community, such as the degree of artificial partnership needed and wanted (e.g., [60]), the design of the dialogue between a human and a computer (e.g., [40]), the adaptivity of artificial partners depending on the current needs of the group or community (e.g., [26,38]), and the algorithmic governance needed in such human-computer collaborations (e.g., [9]). The proposed method for the design of human-computer configurations might help to investigate these collaborative contexts more precisely, where humans and computers, i.e., machines, collaborate to achieve a shared goal [30].…”

Section: Discussion Of the Methods For Human-computer Configuration Dementioning

confidence: 99%

Discovering the Sweet Spot of Human-Computer Configurations

Mackeprang

Müller-Birn

Stauss

2019

Proc. ACM Hum.-Comput. Interact.

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Interactive intelligent systems, i.e., interactive systems that employ AI technologies, are currently present in many parts of our social, public and political life. An issue reoccurring often in the development of these systems is the question regarding the level of appropriate human and computer contributions. Engineers and designers lack a way of systematically defining and delimiting possible options for designing such systems in terms of levels of automation. In this paper, we propose, apply and reflect on a method for human-computer configuration design. It supports the systematic investigation of the design space for developing an interactive intelligent system. We illustrate our method with a use case in the context of collaborative ideation. Here, we developed a tool for information extraction from idea content. A challenge was to find the right level of algorithmic support, whereby the quality of the information extraction should be as high as possible, but, at the same time, the human effort should be low. Such contradicting goals are often an issue in system development; thus, our method proposed helped us to conceptualize and explore the design space. Based on a critical reflection on our method application, we want to offer a complementary perspective to the value-centered design of interactive intelligent systems. Our overarching goal is to contribute to the design of so-called hybrid systems where humans and computers are partners. intelligent systems are AI 1 -supported systems, with which people interact when selecting songs, reading news or searching for products. Such systems should be examined by considering both the human and the system. Jameson and Riedl call this a 'binocular view' of interactive intelligent systems because the system's design includes algorithm design with interaction design, on the one hand, and a combined evaluation of a system's performance and human behavior, on the other hand [33]. However, existing research provides little guidance on how we should design interactive intelligent systems. Should a task be carried out by a human or a computer? What is an appropriate level of interaction vs. integration? Is human labor more preferable than automated action? How can we make an informed decision about allocating the task to either one or the other? How can we evaluate our decision? A proposal of a method for elaborating this spectrum is still missing.The authors have experienced this issue in the context of information extraction from ideas in the research area of collaborative ideation. In addition to complete manual approaches for making sense of an idea's content [4], algorithmic approaches were proposed which describe the content of the ideas statistically (e.g., [6]). However, both perspectives (manual vs. automatic) have their limitations; thus, the question is whether a 'sweet spot' that emphasizes the advantages of both perspectives exists and how such a 'sweet spot' can be identified. A 'sweet spot' defines a compromise between the often contradictory evaluative...

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Section: Discussion Of the Methods For Human-computer Configuration Dementioning

confidence: 99%

Discovering the Sweet Spot of Human-Computer Configurations

Mackeprang

Müller-Birn

Stauss

2019

Proc. ACM Hum.-Comput. Interact.

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“…On the one hand, users are prompted to engage in conversational repair activities by replying to CAs' uncertainty expressions (with or without alternatives) or rephrasing their input [18,20]. On the other hand, employees are involved to avoid breakdowns by selecting an appropriate answer from CAs' suggestions [12,37]. Nevertheless, existing approaches do not yet provide effective strategies for escalating requests to HSAs, (1) for which repeated repair attempts have failed or (2) which require employee handling as an (interim) result of a conversation.…”

Section: Related Work 21 Conversational Agentsmentioning

confidence: 99%

“…To avoid negative effects in terms of customer dissatisfaction due to additional waiting times, these solutions should address the challenge of realtime support [19]. The seamless handover of requests from CAs to HSAs as a recovery strategy addresses the suggestion in the literature of transferring requests to employees, if CAs' capacities are exceeded [12,17,32,38].…”

Section: Related Work 21 Conversational Agentsmentioning

confidence: 99%

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Hybrid Service Recovery: Design for Seamless Inquiry Handovers between Conversational Agents and Human Service Agents

Poser¹,

Singh²,

Bittner³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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The effort of companies to deploy conversational agents (CAs) for customer self-service has been renewed due to their recent technological improvements. Despite their efficiency in processing recurring simple customer inquiries, limited capabilities of CAs to handle complex inquiries still lead to service failure and unsatisfied customers. Therefore, we propose a hybrid service recovery strategy with real-time handovers of inquiries from CAs to human service agents (HSAs), if CAs' capabilities are exceeded. Following a Design Science Research (DSR) approach, we present design principles (DPs) for the inquiry handover scenario, based on meta-requirements (MRs) derived from literature and expert interviews. By evaluating the DPs via prototype instantiation and process modulation, the suitability and interdependence of CAs' information collection activities and information presentation for handover could be verified.

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“…Conversational user interfaces (CUIs), either controlled through voice as Intelligent Personal Assistants (IPAs), or through text as chatbots, are regaining popularity after an initial slump caused by over-estimation of their potential capabilities [10]. Rather than relying on more social forms of conversation, which may not be desirable [5], these assistants have found a niche in both commercial and domestic contexts through simple, task-oriented adjacencypair dialogues.…”

Section: Introductionmentioning

confidence: 99%

Don't Believe The Hype!

Rough

Cowan

2020

Proceedings of the 2nd Conference on Conversational User Interfaces

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Following the initial hype and high expectations of conversational user interfaces (CUIs), a number of creation tools have emerged to simplify development of these complex systems. These have the potential to democratise and expand application development to those without programming skills. However, while such tools allow end-user developers to build language understanding and dialog management capability into a CUI application, actually fulfilling or executing an action still requires programmatic API integration. In this paper, we look at how CUI builders that claim to be "no code required" struggle to yield more than toy examples, with an aim to provoke the community to develop better tools for CUI creation. CCS CONCEPTS • Human-centered computing → Natural language interfaces; User interface design.

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Chatbots, Humbots, and the Quest for Artificial General Intelligence

Cited by 192 publications

References 17 publications

Discovering the Sweet Spot of Human-Computer Configurations

Discovering the Sweet Spot of Human-Computer Configurations

Hybrid Service Recovery: Design for Seamless Inquiry Handovers between Conversational Agents and Human Service Agents

Don't Believe The Hype!

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