Skip to main content
SpringerLink
Log in

Australian public understandings of artificial intelligence

  • Open Forum
  • Published:
AI & SOCIETY Aims and scope Submit manuscript

Abstract

In light of the growing need to pay attention to general public opinions and sentiments toward AI, this paper examines the levels of understandings amongst the Australian public toward the increased societal use of AI technologies. Drawing on a nationally representative survey of 2019 adults across Australia, the paper examines how aware people consider themselves to be of recent developments in AI; variations in popular conceptions of what AI is; and the extent to which levels of support for AI are liable to alter with additional exposure to information about AI. While a majority of respondents consider themselves to have little knowledge and familiarity with the topic of AI, the survey nevertheless finds considerable range of relatively ‘plausible’ basic understandings of what AI is. Significantly, repeated questioning highlights a willingness among many people to reassess their opinions once having received further information about AI, and being asked to think through issues relating to AI and society. These patterns remain relatively consistent, regardless of respondents’ political orientation, income, social class and other demographic characteristics. As such, the paper concludes by considering how these findings provide support for the development of public education efforts to further enhance what might be termed ‘public understanding of AI’.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of data and material

Survey dataset is publicly available: https://doi.org/10.26180/13240619..

References

  • Adams E, Burall S (2019) How to stimulate effective public engagement on the ethics of artificial intelligence. Involve

  • Aoki N (2020) An experimental study of public trust in AI chatbots in the public sector. Gov Inf Q 37(4):101490

    Article  Google Scholar 

  • Balaram B, Greenham T, Leonard J (2018). Artificial intelligence: real public engagement. Royal Society of the Arts

  • Biemer P (2010) Total survey error: design, implementation, and evaluation. Public Opin Q 74(5):817–848

    Article  Google Scholar 

  • Bradford B, Yesberg J, Jackson J, Dawson P (2020) Live facial recognition: trust and legitimacy as predictors of public support for police use of new technology. Br J Criminol 60(6):1502–1522

    Google Scholar 

  • Braithwaite V (2020) Beyond the bubble that is Robodebt: how governments that lose integrity threaten democracy. Aust J Soc Issues 55(3):242–259

    Article  Google Scholar 

  • Cave S, Coughlan K, Dihal K (2019) Scary robots: examining public responses to AI. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. ACM, 331–337

  • Chandler J, Rosenzweig C, Moss A (2019) Online panels in social science research: expanding sampling methods beyond Mechanical Turk. Behav Res 51:2022–2038

    Article  Google Scholar 

  • Collins H, Evans R (2008) Rethinking expertise. University of Chicago Press, Chicago

    Google Scholar 

  • Collins H, Pinch T (2014) The Golem at large. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Cui D, Wu F (2019) The influence of media use on public perceptions of artificial intelligence in China. Information Development. 0266666919893411

  • Dillon S, Collett C (2019) AI and gender: four proposals for future research. Leverhulme Centre for the Future of Intelligence

  • Fast E, Horvitz E (2017) Long-term trends in the public perception of artificial intelligence. In: 31st AAAI conference on Artificial Intelligence, San Francisco, February 4–9

  • Galanos V (2019) Exploring expanding expertise. Technol Anal Strateg Manag 31(4):421–432

    Article  Google Scholar 

  • Galloway K (2017) Big data: a case study of disruption and government power. Altern Law J 42(2):89–95

    Article  Google Scholar 

  • Gebru T (2020) Race and gender. In: Pasquale F, Dubber M, Das S (eds.)

  • Geirbo H (2017) Smart environments? Reflections on the role of metaphors in IS. Scandinavian Journal of Information Systems 29(2):art. 4

  • Giuliano R (2020) Echoes of myth and magic in the language of Artificial Intelligence. AI Soc 35:1009–1024

    Article  Google Scholar 

  • Guihot M, Matthew A, Suzor N (2017) Nudging robots: innovative solutions to regulate Artificial Intelligence. Vanderbilt J Entertain Technol Law 20(2):385–456

    Google Scholar 

  • Huggins A (2019) We need human oversight of machine decisions to stop robo-debt drama. The Conversation, 2 July.

  • Hulse L, Xie H, Galea E (2018) Perceptions of autonomous vehicles. Saf Sci 102:1–13

    Article  Google Scholar 

  • Ipsos/MORI (2017) Public views of machine learning. Royal Society

  • James A, Whelan A (2021) ‘Ethical’ artificial intelligence in the welfare state: discourse and discrepancy in Australian social services. Crit Soc Policy. https://doi.org/10.1177/0261018320985463

    Article  Google Scholar 

  • Kanal L, Lemmer J (2014) Uncertainty in artificial intelligence. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Kelley P, Yang Y, Heldreth C, Moessner C, Sedley A, Kramm A, Woodruff A (2019) Happy and assured that life will be easy 10 years from now: perceptions of Artificial Intelligence in 8 countries. arXiv preprint. arXiv:2001.00081.

  • Krafft P, Young M, Katell M, Huang K, Bugingo G (2020) Defining AI in policy versus practice. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 72–78

  • Leetaru K (2019) Why is the public so AI-illiterate? Forbes

  • Leite I, Martinho C, Paiva A (2013) Social robots for long-term interaction: a survey. Int J Soc Robot 5(2):291–308

    Article  Google Scholar 

  • Licht K, Licht J (2020) Artificial intelligence, transparency, and public decision-making. AI Soc 35:917–926

    Article  Google Scholar 

  • Lobera J, Fernández Rodríguez C, Torres-Albero C (2020) Privacy, values and machines. Commun Stud 71(3):448–465

    Article  Google Scholar 

  • Lockey S, Gillespie N, Curtis C (2020) Trust in artificial intelligence: Australian insights 2020. KPMG

  • Long D, Magerko B (2020) What is AI literacy? In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16

  • Luccioni A, Bengio Y (2020) On the morality of Artificial Intelligence [Commentary]. IEEE Technol Soc Mag 39(1):16–25

    Article  Google Scholar 

  • Lupton D, Michael M (2017) ‘Depends on who’s got the data’: public understandings of personal digital dataveillance. Surveill Soc 15(2):254–268

    Article  Google Scholar 

  • Malka A, Lelkes Y, Soto C (2019) Are cultural and economic conservatism positively correlated? Brit J Polit Sci 49(3):1045–1069

    Article  Google Scholar 

  • Martin A, Donovan K (2015) New surveillance technologies and their publics. Public Underst Sci 24(7):842–857

    Article  Google Scholar 

  • McCorduck P (1979) Machines who think. Freeman

    Google Scholar 

  • OECD (2019) Recommendation of the Council on Artificial Intelligence, OECD/LEGAL/0449 www.oecd.ai/ai-principles

  • Pacheco J, Maltby E (2017) The role of public opinion—does it influence the diffusion of ACA decisions? J Health Polit Policy Law 42(2):309–340

    Article  Google Scholar 

  • Peng Y (2020) The ideological divide in public perceptions of self-driving cars. Public Underst Sci 29(4):436–451

    Article  Google Scholar 

  • Ridley M, Pawlick-Potts D (2021) Algorithmic literacy and the role for libraries. ITAL. https://doi.org/10.6017/ital.v40i2.12963

    Article  Google Scholar 

  • Russell S, Norvig P (1995) Artificial intelligence: a modern approach. Prentice Hall

    MATH  Google Scholar 

  • Serholt S (2018) Breakdowns in children’s interactions with a robotic tutor. Comput Human Behav 81:250–264

    Article  Google Scholar 

  • Sheppard J, Gray M (2017) Australians largely support science, but not all see the benefits. The Conversation

  • Smith T (2011) Refining the total survey error perspective. Int J Public Opinion Res 23(4):464–484

    Article  Google Scholar 

  • Taulli T (2020) Facial Recognition Bans: What Do They Mean For AI? Forbes. https://www.forbes.com/sites/tomtaulli/2020/06/13/facial-recognition-bans-what-do-they-mean-for-ai-artificial-intelligence

  • Tennant C, Stares S, Howard S (2019) Public discomfort at the prospect of autonomous vehicles. Transp Res Part F 64:98–118

    Article  Google Scholar 

  • Tomboulides D, Dooney J, Mcintyre L, Abbott T (2019) Strategies to inform the Swiss public on Artificial Intelligence. https://digitalcommons.wpi.edu/iqp-all/5583/

  • Tonkin C (2021) Robodebt was an AI ethics disaster. ACS Information Age. https://ia.acs.org.au/article/2021/robodebt-was-an-ai-ethics-disaster.html

  • Van Dijck J (2003) After the ‘two cultures’ toward a ‘(multi) cultural’ practice of science communication. Sci Commun 25(2):177–190

    Article  Google Scholar 

  • Vartiainen H, Toivonen T, Jormanainen I, Kahila J, Tedre M, Valtonen T (2021) Machine learning for middle schoolers. Int J Child-Comput Interact 29:100281

    Article  Google Scholar 

  • Wolf C, Joye D, Smith T, Fu Y (2016) The Sage handbook of survey methodology. SAGE, London

    Book  Google Scholar 

  • Wynne B (2014) Further disorientation in the hall of mirrors. Public Underst Sci 23(1):60–70

  • Yigitcanlar T, Kankanamge N, Regona M, Ruiz Maldonado A, Rowan B, Ryu A et al (2020) Artificial intelligence technologies and related urban planning and development concepts. JOItmC 6(4):187

    Article  Google Scholar 

  • Zhai Y, Yan J, Zhang H, Lu W (2020) Tracing the evolution of AI. IDD. https://doi.org/10.1108/IDD-01-2020-0007

    Article  Google Scholar 

  • Zhang B (2019) Public opinion lessons for AI regulation. Brookings Institute. www.brookings.edu/research/public-opinion-lessons-for-ai-regulation

  • Zhang B, Dafoe A (2019) Artificial intelligence: American attitudes and trends. available at SSRN 3312874

  • Zhou Y, Danks D (2020) ‘Different ‘intelligibility’ for different folks. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 194–199

Download references

Funding

This research was supported by a research grant from the Monash Data Futures Institute.

Author information

Authors and Affiliations

  1. Monash Data Futures Institute, Monash University, 19 Ancora Imparo Way, Melbourne, VIC, 3800, Australia

    Neil Selwyn

  2. School of Education, Culture & Society, Monash University, Melbourne, Australia

    Beatriz Gallo Cordoba

Authors
  1. Neil Selwyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beatriz Gallo Cordoba
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Selwyn took primary responsibility for the writing of the introduction, methods, discussion and conclusions. Gallo Cordoba took primarily responsibility for the data analysis and presentation. Both contributed equally to the preparation of the paper.

Corresponding author

Correspondence to Neil Selwyn.

Ethics declarations

Conflict of interest

There is no conflict of interest to report.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Selwyn, N., Gallo Cordoba, B. Australian public understandings of artificial intelligence. AI & Soc 37, 1645–1662 (2022). https://doi.org/10.1007/s00146-021-01268-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00146-021-01268-z

Keywords

Search

Navigation