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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. repec:plo:pone00:0176325 is not listed on IDEAS
    5. 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.
    6. repec:plo:pone00:0194811 is not listed on IDEAS
    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.
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