IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0268081.html
   My bibliography  Save this article

The Answer Bot Effect (ABE): A powerful new form of influence made possible by intelligent personal assistants and search engines

Author

Listed:
  • Robert Epstein
  • Vivian Lee
  • Roger Mohr
  • Vanessa R Zankich

Abstract

We introduce and quantify a relatively new form of influence: the Answer Bot Effect (ABE). In a 2015 report in PNAS, researchers demonstrated the power that biased search results have to shift opinions and voting preferences without people’s knowledge–by up to 80% in some demographic groups. They labeled this phenomenon the Search Engine Manipulation Effect (SEME), speculating that its power derives from the high level of trust people have in algorithmically-generated content. We now describe three experiments with a total of 1,736 US participants conducted to determine to what extent giving users “the answer”–either via an answer box at the top of a page of search results or via a vocal reply to a question posed to an intelligent personal assistant (IPA)–might also impact opinions and votes. Participants were first given basic information about two candidates running for prime minister of Australia (this, in order to assure that participants were “undecided”), then asked questions about their voting preferences, then given answers to questions they posed about the candidates–either with answer boxes or with vocal answers on an Alexa simulator–and then asked again about their voting preferences. The experiments were controlled, randomized, double-blind, and counterbalanced. Experiments 1 and 2 demonstrated that answer boxes can shift voting preferences by as much as 38.6% and that the appearance of an answer box can reduce search times and clicks on search results. Experiment 3 demonstrated that even a single question-and-answer interaction on an IPA can shift voting preferences by more than 40%. Multiple questions posed to an IPA leading to answers that all have the same bias can shift voting preferences by more than 65%. Simple masking procedures still produced large opinion shifts while reducing awareness of bias to close to zero. ABE poses a serious threat to both democracy and human autonomy because (a) it produces large shifts in opinions and voting preferences with little or no user awareness, (b) it is an ephemeral form of influence that leaves no paper trail, and (c) worldwide, it is controlled almost exclusively by just four American tech companies. ABE will become a greater threat as people increasingly rely on IPAs for answers.

Suggested Citation

  • Robert Epstein & Vivian Lee & Roger Mohr & Vanessa R Zankich, 2022. "The Answer Bot Effect (ABE): A powerful new form of influence made possible by intelligent personal assistants and search engines," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-26, June.
    • Handle: RePEc:plo:pone00:0268081
    DOI: 10.1371/journal.pone.0268081
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0268081
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0268081&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0268081?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Diana-Cezara Toader & Grațiela Boca & Rita Toader & Mara Măcelaru & Cezar Toader & Diana Ighian & Adrian T. Rădulescu, 2019. "The Effect of Social Presence and Chatbot Errors on Trust," Sustainability, MDPI, vol. 12(1), pages 1-24, December.
    2. repec:plo:pone00:0176325 is not listed on IDEAS
    3. repec:plo:pone00:0194811 is not listed on IDEAS
    4. Alexander Haas & Julian Unkel, 2017. "Ranking versus reputation: perception and effects of search result credibility," Behaviour and Information Technology, Taylor & Francis Journals, vol. 36(12), pages 1285-1298, December.
    5. Wenlong Sun & Olfa Nasraoui & Patrick Shafto, 2020. "Evolution and impact of bias in human and machine learning algorithm interaction," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.
    6. Ujué Agudo & Helena Matute, 2021. "The influence of algorithms on political and dating decisions," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
    7. Anastasia Kozyreva & Philipp Lorenz-Spreen & Ralph Hertwig & Stephan Lewandowsky & Stefan M. Herzog, 2021. "Public attitudes towards algorithmic personalization and use of personal data online: evidence from Germany, Great Britain, and the United States," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Robert Epstein & Alex Flores, 2024. "The Video Manipulation Effect (VME): A quantification of the possible impact that the ordering of YouTube videos might have on opinions and voting preferences," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-25, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Robert Epstein & Alex Flores, 2024. "The Video Manipulation Effect (VME): A quantification of the possible impact that the ordering of YouTube videos might have on opinions and voting preferences," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-25, November.
    2. Goonj Mohan, 2024. "Regulating a Social Media Platform in the Data Economy," UB School of Economics Working Papers 2024/477, University of Barcelona School of Economics.
    3. Sophie Minihold & Fabio Votta, 2024. "Accepting Exclusion: Examining the (Un)Intended Consequences of Data-Driven Campaigns," Media and Communication, Cogitatio Press, vol. 12.
    4. Vaclav Moravec & Nik Hynek & Marinko Skare & Beata Gavurova & Volodymyr Polishchuk, 2025. "Algorithmic personalization: a study of knowledge gaps and digital media literacy," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
    5. Diane Felmlee & Sara Francisco & Melissa Hardy, 2024. "Networks of Negativity: Gaining Attention Through Cyberbullying," IJERPH, MDPI, vol. 21(12), pages 1-18, December.
    6. Pinochet, Luis Hernan Contreras & de Gois, Fernanda Silva & Pardim, Vanessa Itacaramby & Onusic, Luciana Massaro, 2024. "Experimental study on the effect of adopting humanized and non-humanized chatbots on the factors measure the intensity of the user's perceived trust in the Yellow September campaign," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    7. repec:osf:osfxxx:284sm_v1 is not listed on IDEAS
    8. Katharina Baum & Olga Abramova & Stefan Meißner & Hanna Krasnova, 2023. "The effects of targeted political advertising on user privacy concerns and digital product acceptance: A preference-based approach," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-17, December.
    9. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    10. Goonj Mohan, 2024. "The Data Economy and Polarization on Social Media," UB School of Economics Working Papers 2024/462, University of Barcelona School of Economics.
    11. Robert Epstein & Ji Li, 2024. "Can biased search results change people’s opinions about anything at all? a close replication of the Search Engine Manipulation Effect (SEME)," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-17, March.
    12. Robert Epstein & Christina Tyagi & Hongyu Wang, 2023. "What would happen if twitter sent consequential messages to only a strategically important subset of users? A quantification of the Targeted Messaging Effect (TME)," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-25, July.
    13. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    14. M. Leszczynska & D. Baltag, 2024. "“Can I have it non-personalised?” An Empirical Investigation of Consumer Willingness to Share Data for Personalized Services and Ads," Journal of Consumer Policy, Springer, vol. 47(3), pages 345-372, September.
    15. Junyan Zhu & Katharine Dommett & Tom Stafford, 2025. "What makes online political ads unacceptable? Interrogating public attitudes to inform regulatory responses," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    16. S. Mills & S. Costa & C. R. Sunstein, 2023. "AI, Behavioural Science, and Consumer Welfare," Journal of Consumer Policy, Springer, vol. 46(3), pages 387-400, September.
    17. Gkinko, Lorentsa & Elbanna, Amany, 2023. "Designing trust: The formation of employees’ trust in conversational AI in the digital workplace," Journal of Business Research, Elsevier, vol. 158(C).
    18. Rizomyliotis, Ioannis & Kastanakis, Minas N. & Giovanis, Apostolos & Konstantoulaki, Kleopatra & Kostopoulos, Ioannis, 2022. "“How mAy I help you today?” The use of AI chatbots in small family businesses and the moderating role of customer affective commitment," Journal of Business Research, Elsevier, vol. 153(C), pages 329-340.
    19. Kelvin Leong & Anna Sung, 2024. "Gender stereotypes in artificial intelligence within the accounting profession using large language models," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    20. Cláudia Lessa & Arnaldo Coelho, 2024. "Building Trust in Higher Education Institutions: Using Congruence to Overcome Scepticism and Increase Credibility, Reputation, and Student Employability Through CSR," Corporate Reputation Review, Palgrave Macmillan, vol. 27(1), pages 18-32, February.
    21. Georg Pardi & Steffen Gottschling & Yvonne Kammerer, 2024. "The influence of knowledge type and source reputation on preferences for website or video search results," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(5), pages 521-537, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0268081. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.