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International Journal of Social Robotics

, Volume 9, Issue 3, pp 379–384 | Cite as

Fear of Autonomous Robots and Artificial Intelligence: Evidence from National Representative Data with Probability Sampling

  • Yuhua Liang
  • Seungcheol Austin Lee
Article

Abstract

People vary in the extent to which they report fear toward robots, especially when they perceive that the robot is autonomous or has artificial intelligence. This research examines a specific form of sociological fear, which we name as fear of autonomous robots and artificial intelligence (FARAI). This fear may serve to affect how people will respond to and interact with robots. Applying data from a nationally representative dataset with probability sampling (N = 1541), research questions examine (1) the extent and frequency of FARAI, (2) demographic and media exposure predictors, and (3) correlates with other types of fear (i.e., loneliness, drones, and unemployment). A latent class analysis reveals that approximately 26% of participants reported experiencing a heightened level of FARAI. Demographic analyses show that FARAI is connected to participant sex, age, education, and household income; albeit these effects were small. Media exposure to science fiction predicts FARAI above and beyond the demographic variables. Correlational results indicate that FARAI is associated with other types of fear, including loneliness, becoming unemployed, and drone use. In sum, these findings render a much needed glimpse and update regarding how much individuals fear robots and artificial intelligence.

Keywords

Fear Autonomous robots Artificial intelligence Survey National sample 

References

  1. 1.
    American Psychiatric Association (2003) Diagnostic and statistical manual of mental disorders, 5th edn. ArlingtonGoogle Scholar
  2. 2.
    Celeux G, Soromenho G (1996) An entropy criterion for assessing the number of clusters in a mixture model. J Classif 13(2):195–212MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ (1999) Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods 4(3):272–299Google Scholar
  4. 4.
    Geer JH (1965) The development of a scale to measure fear. Behav Res Ther 3(1):45–53CrossRefGoogle Scholar
  5. 5.
    Kaiser HF (1960) The application of electronic computers to factor analysis. Educ Psychol Meas 20(1):141–151CrossRefGoogle Scholar
  6. 6.
    Liang Y, Lee SA (2016) Advancing the strategic messages affecting robot trust effect: the dynamic of user-and robot-generated content on human-robot trust and interaction outcomes. Cyberpsychol Behav Soc Netw 19(9):538–544Google Scholar
  7. 7.
    Muthén LK, Muthén BO (2010) Mplus user’s guide: statistical analysis with latent variables: user’s guide. Muthén & Muthén, Los AngelesGoogle Scholar
  8. 8.
    Nomura T, Kanda T, Suzuki T (2006) Experimental investigation into influence of negative attitudes toward robots on human–robot interaction. AI Soc 20(2):138–150CrossRefGoogle Scholar
  9. 9.
    Park HS, Dailey R, Lemus D (2002) The use of exploratory factor analysis and principal components analysis in communication research. Hum Commun Res 28(4):562–577CrossRefGoogle Scholar
  10. 10.
    Smith A, Anderson J (2014) AI, robotics, and the future of jobs. Pew Research Center. Washington. http://www.pewinternet.org/2014/08/06/future-of-jobs/
  11. 11.
    Tudor A (2003) A (macro) sociology of fear? Sociol Rev 51(2):238–256CrossRefGoogle Scholar
  12. 12.
    U.S. Census Bureau (2016). Household income: 2015. Retrived from https://www.census.gov/library/publications/2016/acs/acsbr15-02.html
  13. 13.
    Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204CrossRefGoogle Scholar
  14. 14.
    Wang J, Wang X (2012) Structural equation modeling: applications using Mplus. Wiley, HobokenCrossRefzbMATHGoogle Scholar
  15. 15.
    Witte K (1992) Putting the fear back into fear appeals: the extended parallel process model. Commun Monogr 59(4):329–349CrossRefGoogle Scholar
  16. 16.
    Zillmann D, Bryant J (2013) Selective exposure to communication. Routledge, LondonGoogle Scholar
  17. 17.
    Zwick WR, Velicer WF (1982) Factors influencing four rules for determining the number of components to retain. Multivar Behav Res 17(2):253–269CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Chapman UniversityOrangeUSA
  2. 2.Northern Kentucky UniversityHighland HeightsUSA

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