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Folk Beliefs of Artificial Intelligence and Robots

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Abstract

Artificial intelligence (AI) and robots have the potential to revolutionize society, with impacts ranging from the broadest reaches of industry and policy to the minutiae of daily life. The extent to which AI-based technologies can bring benefits to human society depends on how people perceive them––folk beliefs of AI and robots. The present paper aims to gain insights into people’s perspectives on artificial intelligence and robots by examining their folk beliefs. In Study 1, we explored folk beliefs regarding general artificial intelligence and robots using metaphor nomination (Phase 1, N = 99), factor analysis (Phase 2, N = 267), and semantic analysis (Phase 3). Results indicated three primary folk beliefs for AI: the unknown, the assistants, and the machines. For robots, three primary folk beliefs emerged: the assistants, the companions, and the tools. In Study 2, we investigated folk beliefs about robots in various application contexts through free listing (Phase 1, N = 82) and factor analysis (Phase 2, N = 300). Results revealed four folk beliefs for companion robots: companion ability, applicable target, social consequence, and technology. Additionally, four folk beliefs emerged for education robots: educational ability, advantage, disadvantage, and technology, while medical robots were associated with five folk beliefs: medical ability, advancement, social consequence, disadvantage, and technology. This research is the first step in examining how ordinary people conceptualize artificial intelligence and robots through folk theories, unveiling several directions for future research reference. Our findings also revealed that lay people’s perceptions of artificial intelligence and robots are shaped by social cognitive processes. This also implies that the methods of folk theories can be utilized to investigate people’s social cognitive processes. The current study carries practical significance for the designers and manufacturers of AI and robots, guiding aspects such as the professional capabilities of artificial intelligence and robots, potential negative social consequences, and the needs of specific user groups.

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Data Availability

The authors will share data from the study upon reasonable request to the corresponding author.

References

  1. Glikson E, Woolley AW (2020) Human trust in artificial intelligence: Review of empirical research. Acad Manag Ann 14(2):627–660. https://doi.org/10.5465/annals.2018.0057

    Article  Google Scholar 

  2. Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7

    Article  Google Scholar 

  3. Kozyreva A, Lorenz-Spreen P, Hertwig R, Lewandowsky S, Herzog SM (2021) Public attitudes towards algorithmic personalization and use of personal data online: Evidence from Germany, Great Britain, and the United States. Humanit Soc Sci 8(1):1–11. https://doi.org/10.1057/s41599-021-00787-w

    Article  Google Scholar 

  4. Agomuoh F, Larsen L (2023) ChatGPT: How to use the AI chatbot that’s changing everything. https://www.digitaltrends.com/computing/how-to-use-openai-chatgpt-text-generation-chatbot/. Accessed June 11 2023

  5. Xu LY, Yu F (2020) Factors that influence robot acceptance. Chin Sci Bull 65(6):496–510. https://doi.org/10.1360/TB-2019-0136

    Article  Google Scholar 

  6. Heerink M, Kröse B, Evers V, Wielinga B (2010) Relating conversational expressiveness to social presence and acceptance of an assistive social robot. Virtual Real 14:77–84. https://doi.org/10.1007/s10055-009-0142-1

    Article  Google Scholar 

  7. Shin DH, Choo H (2011) Modeling the acceptance of socially interactive robotics: Social presence in human–robot interaction. Interact Stud 12(3):430–460. https://doi.org/10.1075/is.12.3.04shi

    Article  Google Scholar 

  8. Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed June 11 2023

  9. Cadario R, Longoni C, Morewedge CK (2021) Understanding, explaining, and utilizing medical artificial intelligence. Nat Hum Behav 5(12):1636–1642. https://doi.org/10.1038/s41562-021-01146-0

    Article  Google Scholar 

  10. Złotowski J, Yogeeswaran K, Bartneck C (2017) Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. Int J Hum Comput 100:48–54. https://doi.org/10.1016/j.ijhcs.2016.12.008

    Article  Google Scholar 

  11. Piçarra N, Giger JC, Pochwatko G, Gonçalves G (2016) Making sense of social robots: A structural analysis of the layperson’s social representation of robots. Eur Rev Appl Psychol 66(6):277–289. https://doi.org/10.1016/j.erap.2016.07.001

    Article  Google Scholar 

  12. Smith A, Anderson M (2017) Automation in Everyday Life. Washington, DC: Pew Research Center. http://www.pewinternet.org/2017/10/04/automationin-everyday-life/. Accessed June 11 2023

  13. Złotowski J, Sumioka H, Nishio S, Glas DF, Bartneck C, Ishiguro H (2016) Appearance of a robot affects the impact of its behaviour on perceived trustworthiness and empathy. Paladyn J Behav Robot 7(1):55–66. https://doi.org/10.1515/pjbr-2016-0005

    Article  Google Scholar 

  14. Davis RB, Maher CA, Noddings N (1990) Constructivist views of the teaching and learning of mathematics. National Council for the Teaching of Mathematics, Reston

  15. Dewey J (1938) Experience and education. Simon and Schuster, New York

    Google Scholar 

  16. Malle BF (2011) Attribution theories: How people make sense of behavior. In: Chadee D (ed) Theories in social psychology. Wiley Blackwell, Hoboken, pp 72–95

  17. Huang SA, Hancock J, Tong ST (2022) Folk theories of online dating: exploring people’s beliefs about the online dating process and online dating algorithms. Soc Med Soc 8(2):20563051221089560. https://doi.org/10.1177/20563051221089561

    Article  Google Scholar 

  18. Malle BF (1999) How people explain behavior: a new theoretical framework. Pers Soc Psychol Rev 3(1):23–48. https://doi.org/10.1207/s15327957pspr0301_2

    Article  Google Scholar 

  19. Gelman SA, Legare CH (2011) Concepts and folk theories. Annu Rev Anthropol 40:379–398. https://doi.org/10.1146/annurev-anthro-081309-145822

    Article  Google Scholar 

  20. DeVito MA, Birnholtz J, Hancock JT, French M, Liu S (2018) How people form folk theories of social media feeds and what it means for how we study self-presentation. In: 2018 CHI conference on human factors in computing systems, 21–26 April 2018. pp 1–12.

  21. French M, Hancock J (2017) What’s the folk theory? Reasoning about cyber-social systems. SSRN. https://ssrn.com/abstract=2910571. Accessed June 11 2023

  22. Keil FC (2010) The feasibility of folk science. Cogn Sci 34(5):826–862. https://doi.org/10.1111/j.1551-6709.2010.01108.x

    Article  Google Scholar 

  23. Keil FC (2012) Running on empty? How folk science gets by with less. Curr Dir Psychol Sci 21(5):329–334. https://doi.org/10.1177/0963721412453721

    Article  Google Scholar 

  24. Pasquale F (2011) Restoring transparency to automated authority. https://heinonline.org/HOL/LandingPage?handle=hein.journals/jtelhtel9&div=11&id=&page= Accessed June 11 2023

  25. Pasquale F (2015) The black box society: The secret algorithms that control money and information. Harvard University Press, Cambridge

  26. Lakoff G, Johnson M (1980) Metaphors we live by. University of Chicago Press, Chicago

    Google Scholar 

  27. Landau MJ, Meier BP, Keefer LA (2010) A metaphor-enriched social cognition. Psychol Bull 136(6):1045–1067. https://doi.org/10.1037/a0020970

    Article  Google Scholar 

  28. Sease R (2008) Metaphor’s role in the information behavior of humans interacting with computers. Inf Technol Libr 27:9–16. https://doi.org/10.6017/ital.v27i4.3237

    Article  Google Scholar 

  29. Berlin RM, Olson ME, Cano CE, Engel S (1991) Metaphor and psychotherapy. Am J Psychother 45(3):359–367. https://doi.org/10.1176/appi.psychotherapy.1991.45.3.359

    Article  Google Scholar 

  30. Peng KP, Yu F (2012) The Psychophysics of morality: Phenomena. Mech Mean Soc Sci China 204(12):28–45

    Google Scholar 

  31. Sandvig C (2015) Seeing the sort: the aesthetic and industrial Defense of the algorithm. J New Med Caucus 11:35–51

    Google Scholar 

  32. Judge M, Fernando JW, Paladino A, Kashima Y (2020) Folk theories of artifact creation: How intuitions about human labor influence the value of artifacts. Pers Soc Psychol Rev 24(3):195–211. https://doi.org/10.1177/1088868320905763

    Article  Google Scholar 

  33. Manoharan C, De Munck V (2017) The conceptual relationship between love, romantic love, and sex: a free list and prototype study of semantic association. J Mixed Methods Res 11(2):248–265. https://doi.org/10.1177/1558689815602151

    Article  Google Scholar 

  34. Walker LJ, Pitts RC (1998) Naturalistic conceptions of moral maturity. Dev Psychol 34:403–419. https://doi.org/10.1037/0012-1649.34.3.403

    Article  Google Scholar 

  35. Fuchs C, Schreier M, van Osselaer SM (2015) The handmade effect: What’s love got to do with it? J Mark 79(2):98–110. https://doi.org/10.1509/jm.14.0018

    Article  Google Scholar 

  36. May DC, Holler KJ, Bethel CL, Strawderman L, Carruth DW, Usher JM (2017) Survey of factors for the prediction of human comfort with a non-anthropomorphic robot in public spaces. Int J Soc Robot 9:165–180. https://doi.org/10.1007/s12369-016-0390-7

    Article  Google Scholar 

  37. Savela N, Turja T, Oksanen A (2018) Social acceptance of robots in different occupational fields: a systematic literature review. Int J Soc Robot 10(4):493–502. https://doi.org/10.1007/s12369-017-0452-5

    Article  Google Scholar 

  38. Naneva S, Sarda Gou M, Webb TL, Prescott TJ (2020) A systematic review of attitudes, anxiety, acceptance, and trust towards social robots. Int J Soc Robot 12(6):1179–1201 https://doi.org/10.1007/s12369-020-00659-4

    Article  Google Scholar 

  39. Fiske ST, Cuddy AJ, Glick P, Xu J (2002) A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition. J Pers Soc Psychol 82(6):878–902. https://doi.org/10.1037/0022-3514.82.6.878

    Article  Google Scholar 

  40. Fiske ST, Cuddy AJ, Glick P (2007) Universal dimensions of social cognition: Warmth and competence. Trends Cogn 11(2):77–83. https://doi.org/10.1016/j.tics.2006.11.005

    Article  Google Scholar 

  41. Waytz A, Cacioppo J, Epley N (2010) Who sees human? The stability and importance of individual differences in anthropomorphism. Perspect Psychol Sci 5(3):219–232. https://doi.org/10.1177/1745691610369336

    Article  Google Scholar 

  42. Xu LY, Yu F, Wu JH, Han TT, Zhao L (2017) Anthropomorphism: Antecedents and consequences. Adv Psychol Sci 25(11):1942–1954. https://doi.org/10.3724/SP.J.1042.2017.01942

    Article  Google Scholar 

  43. Montoya RM, Horton RS, Kirchner J (2008) Is actual similarity necessary for attraction? A meta-analysis of actual and perceived similarity. J Soc Pers Relat 25(6):889–922. https://doi.org/10.1177/0265407508096700

    Article  Google Scholar 

  44. Murray SL, Holmes JG, Bellavia G, Griffin DW, Dolderman D (2002) Kindred spirits? The benefits of egocentrism in close relationships. J Pers Soc Psychol 82(4):563–581. https://doi.org/10.1037/0022-3514.82.4.563

    Article  Google Scholar 

  45. Prisbell M, Andersen JF (1980) The importance of perceived homophily, level of uncertainty, feeling good, safety, and self-disclosure in interpersonal relationships. Commun Q 28(3):22–33. https://doi.org/10.1080/01463378009369372

    Article  Google Scholar 

  46. Bigman YE, Gray K (2018) People are averse to machines making moral decisions. Cognition 181:21–34. https://doi.org/10.1016/j.cognition.2018.08.003

    Article  Google Scholar 

  47. Bonnefon JF, Shariff A, Rahwan I (2016) The social dilemma of autonomous vehicles. Science 352(6293):1573–1576. https://doi.org/10.1126/science.aaf26

    Article  Google Scholar 

  48. Mosakas K (2021) On the moral status of social robots: considering the consciousness criterion. AI Soc 36:429–443. https://doi.org/10.1007/s00146-020-01002-1

    Article  Google Scholar 

  49. Lovell M (2006) Caring for the elderly: changing perceptions and attitudes. J Vasc Nurs 24(1):22–26. https://doi.org/10.1016/j.jvn.2005.11.001

    Article  Google Scholar 

  50. Felfe C, Lalive R (2018) Does early child care affect children’s development? J Public Econ 159:33–53. https://doi.org/10.1016/j.jpubeco.2018.01.014

    Article  Google Scholar 

  51. Granulo A, Fuchs C, Puntoni S (2019) Psychological reactions to human versus robotic job replacement. Nat Hum Behav 3(10):1062–1069. https://doi.org/10.1038/s41562-019-0670-y

    Article  Google Scholar 

  52. Kiesler S (2005) Fostering common ground in human-robot interaction. In: 14th IEEE International workshop on robot and human interactive communication, 13–15 August 2005. pp 729–734.

  53. Salganik MJ, Levy KE (2015) Wiki surveys: open and quantifiable social data collection. PLoS ONE 10(5):e0123483. https://doi.org/10.1371/journal.pone.0123483

    Article  Google Scholar 

  54. Chen R, Xu P, Song P, Wang M, He J (2019) China has faster pace than Japan in population aging in next 25 years. Biosci Trends 13(4):287–291. https://doi.org/10.5582/bst.2019.01213

    Article  Google Scholar 

  55. Cai Y, Feng W (2021) The social and sociological consequences of China’s one-child policy. Ann Rev Sociol 47:587–606. https://doi.org/10.1146/annurev-soc-090220-032839

    Article  Google Scholar 

  56. Eng A, Chi YK, Gray K (2023) People treat social robots as real social agents. Behavioral Brain Sci 46:e28. https://doi.org/10.1017/S0140525X22001534

    Article  Google Scholar 

  57. Mayer RC, Davis JH, Schoorman FD (1995) An integrative model of organizational trust. Acad Manag Rev 20(3):709–734. https://doi.org/10.5465/amr.1995.9508080335

    Article  Google Scholar 

  58. Hancock PA, Billings DR, Schaefer KE, Chen JY, De Visser EJ, Parasuraman R (2011) A meta-analysis of factors affecting trust in human-robot interaction. Hum Factors 53:517–527. https://doi.org/10.1177/0018720811417254

    Article  Google Scholar 

  59. Kaplan AD, Kessler TT, Brill JC, Hancock PA (2023) Trust in artificial intelligence: Meta-analytic findings. Hum Factors 65(2):337–359. https://doi.org/10.1177/00187208211013988

    Article  Google Scholar 

  60. Ashrafian H (2015) Artificial intelligence and robot responsibilities: Innovating beyond rights. Sci Eng Ethics 21:317–326. https://doi.org/10.1007/s11948-014-9541-0

    Article  Google Scholar 

  61. Korinek A, & Stiglitz JE (2018) Artificial intelligence and its implications for income distribution and unemployment. In The economics of artificial intelligence: an agenda (pp 349–390). University of Chicago Press

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Funding

This work was supported by the National Social Science Foundation of China (Grant No. 20CZX059), and the National Natural Science Foundation of China (Grant No. 72101132).

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Author notes

  1. Liying Xu and Yuyan Zhang have contributed equally to this work.

Authors and Affiliations

  1. Department of Psychology, Wuhan University, Wuhan, 430072, China

    Liying Xu, Yuyan Zhang, Feng Yu & Jiahua Wu

  2. Department of Philosophy, School of Humanities and Social Science, Xi’an Jiaotong University, Xi’an, 710049, China

    Xiaojun Ding

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  1. Liying Xu
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  3. Feng Yu
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Contributions

LX: Conceptualization; Data curation; Formal analysis; Investigation; Methodology. YZ: Writing—original draft. FY: Conceptualization; Project administration; Supervision; Writing—review & editing. JW: Writing—original draft.

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Correspondence to Feng Yu.

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The study was conducted in accordance with the Declaration of Helsinki. The studies involving human participants were reviewed and approved by the Ethics Committee of Wuhan University.

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Appendix

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Table 11 Folk beliefs for artificial intelligence in Study 1
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11,

Table 12 Folk beliefs for robots in Study 1
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Table 13 Folk beliefs for companion robots in Study 2
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Table 14 Folk beliefs for education robots in Study 2
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Table 15 Folk beliefs for medical robots in Study 2
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15

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Xu, L., Zhang, Y., Yu, F. et al. Folk Beliefs of Artificial Intelligence and Robots. Int J of Soc Robotics 16, 429–446 (2024). https://doi.org/10.1007/s12369-024-01097-2

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