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Artificial Intelligence in Conversational Agents: A Study of Factors Related to Perceived Humanness in Chatbots

Published:16 February 2020Publication History

ABSTRACT

Artificial intelligence (AI) is gaining traction in service-oriented businesses in the form of chatbots. A chatbot is a popular type of social AI that uses natural language processing to communicate with users. Past studies have shown discrepancies in terms of whether or not a chatbot should communicate and behave like a human. This article aims to explore these discrepancies in order to provide a theoretical contribution of a list of factors related to perceived humanness in chatbots and how these may consequently lead to a positive user experience. The results suggest that a chatbot should have the following characteristics: avoiding small talk and maintaining a formal tone; identifying itself as a bot and asking how it can help; providing specific information and articulating itself with sophisticated choices of words and well-constructed sentences; asking follow-up questions during decision-making processes and; providing an apology when the context is not comprehensible, followed by a question or a statement to dynamically move a conversation forward. These results may have implications for designers working in the field of AI as well as for the wider debates and the broader discourse around the adoption of AI in society.

References

  1. C. Adam and B. Gaudou. 2016. BDI agents in social simulations: A survey. The Knowledge Engineering Review 31, 3 (2016), 207--238.Google ScholarGoogle ScholarCross RefCross Ref
  2. N. Akma, M. Hafiz, A. Zainal, M. Fairuz, and Z. Adnan. 2018. Review of chatbots design techniques. International Journal of Computer Applications 181, 8 (2018), 7--10.Google ScholarGoogle ScholarCross RefCross Ref
  3. T. Bickmore and J. Cassell. 2005. Social dialogue with embodied conversational agents. Springer, Dordrecht, NL, 23--54.Google ScholarGoogle Scholar
  4. T. Bickmore, H. Trinh, S. Olafsson, T. K. O'Leary, R. Asadi, N. M. Rickles, and R. Cruz. 2018. Patient and consumer safety risks when using conversational assistants for medical information: An observational study of Siri, Alexa, and Google Assistant. Journal of Medical Internet Research 20, 9 (2018), e11510.Google ScholarGoogle ScholarCross RefCross Ref
  5. A. Braga and R. Logan. 2017. The emperor of strong AI has no clothes: Limits to artificial intelligence. Information 8, 4 (2017), 156--177.Google ScholarGoogle ScholarCross RefCross Ref
  6. C. Chakrabarti and G. F. Luger. 2015. Artificial conversations for customer service chatter bots: Architecture, algorithms and evaluation metrics. Expert Systems with Applications 42, 2015 (2015), 6878--6897.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Swedish Research Council. 2017. Good research practice. Report. Swedish Research Council.Google ScholarGoogle Scholar
  8. L. J. Cronbach. 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16, 3 (1951), 297--334.Google ScholarGoogle ScholarCross RefCross Ref
  9. R. Dale. 2016. The return of the chatbots. Natural Language Engineering 22, 5 (2016), 811--817.Google ScholarGoogle ScholarCross RefCross Ref
  10. V. Demeure, R. Niewiadomski, and C. Pelachaud. 2011. How is believability of a virtual agent related to warmth, competence, personification, and embodiment? Presence 20, 5 (2011), 431--448.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. Dix. 2010. Human-computer interaction: A stable discipline, a nascent science, and the growth of the long tail. Interacting with Computers 22, 1 (2010), 13--27.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. P. Dourish. 2004. What we talk about when we talk about context. Personal Ubiquitous Computing 8, 1 (2004), 19--30.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. D. Duijst. 2017. Can we improve the user experience of chatbots with personalisation? Thesis.Google ScholarGoogle Scholar
  14. A. Følstad and P. B. Brandtzaeg. 2017. Chatbots and the new world of HCI. Interactions 24, 4 (2017), 38--42.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. L. Floridi, J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum, C. Luetge, R. Madelin, U. Pagallo, F. Rossi, B. Schafer, P. Valcke, and E. Vayena. 2018. AI4People - An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines 28, 4 (2018), 689--707.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. N. Foulquier, P. Redou, C. Le Gal, B. Rouvière, J-O. Pers, and A. Saraux. 2018. Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: A systematic literature review. Human Vaccines and Immunotherapeutics 14, 11 (2018), 2553--2558.Google ScholarGoogle Scholar
  17. M. Gams, I. Y. Gu, A. Härmä, A. Muñoz, and V. Tam. 2019. Artificial intelligence and ambient intelligence. Journal of Ambient Intelligence and Smart Environments 11, 1 (2019), 71--86.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. T. Hagendorff and K. Wezel. 2019. 15 challenges for AI: or what AI (currently) can't do. AI and Society 3, 2019 (2019), 1--11.Google ScholarGoogle Scholar
  19. J. Hecht. 2018. Meeting people's expectations. Nature Outlook - Digital Revolution 563, 7733 (2018), 141--143.Google ScholarGoogle Scholar
  20. M. Huang and R. Rust. 2017. Artificial intelligence in service. Journal of Service Research 21, 2 (2017), 155--172.Google ScholarGoogle ScholarCross RefCross Ref
  21. M. Jain, P. Kumar, R. Kota, and S. N. Patel. 2018. Evaluating and informing the design of chatbots. In DIS 2018, Session 18: Interacting with Conversational Agents. 895--906.Google ScholarGoogle Scholar
  22. M. H. Jarrahi. 2018. Artificial intelligence and the future of work: Human-AI symbiosos in organizational decision making. Business Horizons 61, 4 (2018), 577--586.Google ScholarGoogle ScholarCross RefCross Ref
  23. L. C. Klopfenstein, S. Delpriori, S. Malatini, and A. Bogliolo. 2017. The rise of bots: A survey of conversational interfaces, patterns and paradigms. In DIS 2017. 555--565.Google ScholarGoogle Scholar
  24. C. Lallemand, G. Gronier, and V. Koenig. 2015. User experience: A concept without consensus? Exploring practitioners' perspectives through an international survey. Computers in Human Behavior 43, 2015 (2015), 35--48.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Q. V. Liao, M. Hussain, P. Chandar, M. Davis, Y. Khazaen, M. P. Crasso, D. Wang, M. Muller, N. S. Shami, and W. Geyer. 2018. All work and no play? Conversations with a question-and-answer chatbot in the wild. In CHI 2018. 1--13.Google ScholarGoogle Scholar
  26. C. L. Lortie and M.J. Guitton. 2011. Judgment of the humanness of an interlocutor is in the eye of the beholder. PLoS ONE 6, 9 (2011), e25085.Google ScholarGoogle ScholarCross RefCross Ref
  27. E. Luger and A. Sellen. 2016. "Like having a really bad PA": The gulf between user expectation and experience of conversational agents. In CHI 2016. 5286--5297.Google ScholarGoogle Scholar
  28. S. Makridakis. 2017. The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures 90, 2017 (2017), 46--60.Google ScholarGoogle ScholarCross RefCross Ref
  29. J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon. 1955/2006. A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine 27, 4(1955/2006), 12--14.Google ScholarGoogle Scholar
  30. D. McDuff and M. Czerwinski. 2018. Designing emotionally sentient agents. Commun. ACM 61, 12 (2018), 74--83.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. McKenney, N. Nieveen, and J. van den Akker. 2006. Design research from a curriculum perspective. Routledge, London, 67--90.Google ScholarGoogle Scholar
  32. M. L. McNeal and D. Newyear. 2013. Introducing chatbots in libraries. Library Technology Reports 49, 8 (2013), 5--10.Google ScholarGoogle Scholar
  33. G. Mone. 2016. The edge of the uncanny. Commun. ACM 59, 9 (2016), 17--19.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. M.Mori. 1970. Bukimi no tani [The uncanny valley]. Energy 7, 4 (1970), 33--35.Google ScholarGoogle Scholar
  35. M. Mori. 2012. The uncanny valley. IEEE Robotics and Automation Magazine 19, 2(2012), 98--100.Google ScholarGoogle ScholarCross RefCross Ref
  36. Y. Mou and K. Xu. 2017. The media inequality: Comparing the initial human-human and human-AI social interactions. Computers in Human Behavior 72, 2017 (2017), 432--440.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. M. Neururer, S. Schlögl, L. Brinkschulte, and A. Groth. 2018. Perceptions on authenticity in chat bots. Multimodal Technologies and Interaction 2, 60 (2018), 1--19.Google ScholarGoogle ScholarCross RefCross Ref
  38. S. Noorunnisa, D. Jarvis, J. Jarvis, and M. Watson. 2019. Application of the GO-RITE BDI framework to human-auto no my teaming: A case study. Journal of Computing and Information Technology 27, 1 (2019), 13--24.Google ScholarGoogle Scholar
  39. E. Norling. 2004. Folk psychology for human modelling: Extending the BDI paradigm.. In AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems. 202--209.Google ScholarGoogle Scholar
  40. E. Paikari and A. van der Hoek. 2018. A framework for understanding chatbots and their future. In Proceedings of 11th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE'18). 13--16.Google ScholarGoogle Scholar
  41. K. Panetta. 2017. Gartner top strategic predictions for 2018 and beyond. (2017). Retrieved September 5, 2019 from https://www.gartner.com/smarterwithgartner/gartner-top- strategic-predictions- for- 2018- and-beyond/Google ScholarGoogle Scholar
  42. J. Pereira and Ó. Díaz. 2019. Using health chatbots for behavior change: A mapping study. Journal of Medical Systems 43, 5 (2019), 1--13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. L. Piccolo, M. Mensio, and H. Alani. 2018. Chasing the chatbots: Directions for interaction and design research. In CONVERSATIONS 2018, 5th International Conference on Internet Science. 1--12.Google ScholarGoogle Scholar
  44. M. Portela and C. Granell-Canut. 2017. A new friend in our smartphone? Observing interactions with chatbots in the search of emotional engagement. In Interacción '17. 1--7.Google ScholarGoogle Scholar
  45. R. Rosales, M. Castañón-Puga, L. Lara-Rosano, D. R. Evans, N. Osuna-Millan, and M. V. Flores-Ortiz. 2017. Modelling the interruption on HCI using BDI agents with the fuzzy perceptions approach: An interactive museum case study in Mexico. Applied Sciences 7, 8 (2017), 1--18.Google ScholarGoogle ScholarCross RefCross Ref
  46. S. Russell and P. Norvig. 2010. Artificial intelligence: A modern approach. Pearson, Upper Saddle River, NJ.Google ScholarGoogle Scholar
  47. R. Schuetzler, M. Grimes, J. S. Giboney, and J. Buckman. 2014. Facilitating natural conversational agent interactions: Lessons from a deception experiment. In 35th International Conference on Information Systems. 1--16.Google ScholarGoogle Scholar
  48. M. Skjuve, I. M. Haugstveit, A. Følstad, and P. B. Brandtzaeg. 2019. Help! Is my chatbot falling into the uncanny valley? An empirical study of user experience in human-chatbot interaction. Human Technology 15, 1 (2019), 30--54.Google ScholarGoogle ScholarCross RefCross Ref
  49. M. Strait, L. Vujovic, V. Floerke, M. Scheutz, and H. Urry. 2015. Too much humanness for human-robot interaction: Exposure to highly humanlike robots elicits aversive responding in observers. In 33rd Annual ACM Conference on Human Factors in Computing Systems. 3593--3602.Google ScholarGoogle Scholar
  50. J. Torresen. 2018. A review of future and ethical perspectives of robotics and AI. Frontiers in Robotics and AI 4, 1 (2018), 1--10.Google ScholarGoogle ScholarCross RefCross Ref
  51. A. Turing. 1950. Computing machinery and intelligence. Mind 59, 236 (1950), 433--460.Google ScholarGoogle ScholarCross RefCross Ref
  52. J. J. H. van den Akker. 1999. Principles and methods of development research. Kluwer Academic Publishers, Dordrecht.Google ScholarGoogle Scholar
  53. A. Vinciarelli, A. Esposito, E. André, F. Bonin, M. Chetouani, J. F. Cohn, M. Cristani, F. Fuhrmann, E. Gilmartin, Z. Hammal, D. Heylen, R. Kaiser, M. Koutsombogera, A. Potamianos, S. Renals, G. Riccardi, and A. A. Salah. 2015. Open challenges in modelling, analysis and synthesis of human behaviour in human-human and human-machine interactions. Cognitive Computation 7, 4 (2015), 397--413.Google ScholarGoogle ScholarCross RefCross Ref
  54. J. B. Walther. 2007. Selective self-presentation in computer-mediated communication: Hyperpersonal dimensions of technology, language, and cognition. Computers in Human Behavior 23, 5 (2007), 2538--2557.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. K. Warwick and H. Shah. 2016. Passing the Turing Test does not mean the end of humanity. Cognitive Computation 8, 3 (2016), 409--419.Google ScholarGoogle ScholarCross RefCross Ref
  56. D. Westerman, A. C. Cross, and P. G. Lindmark. 2018. I believe in a thing called bot: Perceptions of the humanness of "chatbots". Communication Studies 70, 3 (2018), 1--18.Google ScholarGoogle Scholar
  57. Y. Yang, X. Ma, and P. Fung. 2017. Perceived emotional intelligence in virtual agents. In CHI '17 2255--2262.Google ScholarGoogle Scholar
  58. J. Zamora. 2017. I'm sorry, Dave, I'm afraid I can't do that: Chatbot perception and expectations. In HAI 2017. 253--260.Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Other conferences
          AICCC '19: Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference
          December 2019
          216 pages
          ISBN:9781450372633
          DOI:10.1145/3375959

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