11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article
      * , , , ,
      PLoS ONE
      Public Library of Science

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references123

          • Record: found
          • Abstract: found
          • Article: not found

          Dissecting racial bias in an algorithm used to manage the health of populations

          Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Leading questions and the eyewitness report

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Bias in algorithmic filtering and personalization

                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: Investigation
                Role: Formal analysisRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                1 June 2022
                2022
                : 17
                : 6
                : e0268081
                Affiliations
                [001] American Institute for Behavioral Research and Technology, Vista, California, United States of America
                National Institute of Technology Silchar, India, INDIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-7484-6282
                Article
                PONE-D-21-39882
                10.1371/journal.pone.0268081
                9159602
                35648736
                6c00c919-29b7-41ad-a6b5-49c1cb25464c
                © 2022 Epstein et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 December 2021
                : 21 April 2022
                Page count
                Figures: 1, Tables: 8, Pages: 26
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Social Sciences
                Political Science
                Elections
                Research and Analysis Methods
                Database and Informatics Methods
                Information Retrieval
                Computer and Information Sciences
                Computer Networks
                Internet
                Computer and Information Sciences
                Computers
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Intelligence
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Intelligence
                Social Sciences
                Psychology
                Cognitive Psychology
                Intelligence
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Learning
                Human Learning
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Learning
                Human Learning
                Social Sciences
                Psychology
                Cognitive Psychology
                Learning
                Human Learning
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Learning
                Human Learning
                Social Sciences
                Political Science
                Governments
                Custom metadata
                An anonymized version of the data has been posted at https://doi.org/10.5281/zenodo.6537353. Data can also be requested from info@ 123456aibrt.org . The data have been anonymized to comply with requirements of the sponsoring institution’s Institutional Review Board (IRB).

                Uncategorized
                Uncategorized

                Comments

                Comment on this article