IDEAS home Printed from https://ideas.repec.org/a/bla/jinfst/v75y2024i5p581-599.html
   My bibliography  Save this article

Towards improving user awareness of search engine biases: A participatory design approach

Author

Listed:
  • Monica Lestari Paramita
  • Maria Kasinidou
  • Styliani Kleanthous
  • Paolo Rosso
  • Tsvi Kuflik
  • Frank Hopfgartner

Abstract

Bias in news search engines has been shown to influence users' perceptions of a news topic and contribute to the polarisation of society. As a result, there is a need for news search engines that increase user awareness of biases in the search results. While technical approaches have been developed to mitigate biases in search, very few studies have investigated user preferences in interface designs for potentially raising their awareness of biases in news search engines. In this study, we utilized a participatory design methodology to develop eight prototypes with different features that could potentially be used to raise user awareness of biases in news search engines. We conducted three user studies, involving 132 participants with Computer Science backgrounds, to evaluate these prototypes. Our findings indicate the importance of news search engines that (a) inform users of possible biases in the results (bias visualization approach) and (b) allow users to access alternative search results (results‐reranking approach). Our study provides further insights into the strengths and possible risks of each approach, which are important for future research on designing interfaces for raising user awareness of biases in news search engines.

Suggested Citation

  • Monica Lestari Paramita & Maria Kasinidou & Styliani Kleanthous & Paolo Rosso & Tsvi Kuflik & Frank Hopfgartner, 2024. "Towards improving user awareness of search engine biases: A participatory design approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(5), pages 581-599, May.
  • Handle: RePEc:bla:jinfst:v:75:y:2024:i:5:p:581-599
    DOI: 10.1002/asi.24826
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.24826
    Download Restriction: no

    File URL: https://libkey.io/10.1002/asi.24826?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. Nima Kordzadeh & Maryam Ghasemaghaei, 2022. "Algorithmic bias: review, synthesis, and future research directions," European Journal of Information Systems, Taylor & Francis Journals, vol. 31(3), pages 388-409, May.
    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. Dirk Lewandowski & Jutta Haider & Olof Sundin, 2024. "JASIST Special Issue Editorial: Re‐orienting search engine research in information science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(5), pages 503-511, May.

    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. Mallory Avery & Andreas Leibbrandt & Joseph Vecci, 2023. "Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech," Monash Economics Working Papers 2023-09, Monash University, Department of Economics.
    2. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    3. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    4. Le Cheng & Xiuli Liu & Chunlei Si, 2024. "Identifying stance in legislative discourse: a corpus-driven study of data protection laws," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    5. Jiachen Han & Mingming Li & Shi Li & Yingying Hu, 2024. "The widening gender wage gap in the gig economy in China: the impact of digitalisation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
    6. Inga Ulnicane & Aini Aden, 2023. "Power and politics in framing bias in Artificial Intelligence policy," Review of Policy Research, Policy Studies Organization, vol. 40(5), pages 665-687, September.
    7. Jella Pfeiffer & Julia Gutschow & Christian Haas & Florian Möslein & Oliver Maspfuhl & Frederik Borgers & Suzana Alpsancar, 2023. "Algorithmic Fairness in AI," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 209-222, April.
    8. Hanisch, Marvin & Goldsby, Curtis M. & Fabian, Nicolai E. & Oehmichen, Jana, 2023. "Digital governance: A conceptual framework and research agenda," Journal of Business Research, Elsevier, vol. 162(C).
    9. Clement A. Adebamowo & Shawneequa Callier & Simisola Akintola & Oluchi Maduka & Ayodele Jegede & Christopher Arima & Temidayo Ogundiran & Sally N. Adebamowo, 2023. "The promise of data science for health research in Africa," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    10. Lanqing Du & Jinwook Lee, 2023. "Workforce pDEI: Productivity Coupled with DEI," Papers 2311.11231, arXiv.org, revised Dec 2023.

    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:bla:jinfst:v:75:y:2024:i:5:p:581-599. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    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.