skip to main content
10.1145/3450614.3463292acmconferencesArticle/Chapter ViewAbstractPublication PagesumapConference Proceedingsconference-collections
research-article

Transparency Paths - Documenting the Diversity of User Perceptions

Published:22 June 2021Publication History

ABSTRACT

We are living in an era of global digital platforms, eco-systems of algorithmic processes that serve users worldwide. However, the increasing exposure to diversity online – of information and users – has led to important considerations of bias. A given platform, such as the Google search engine, may demonstrate behaviors that deviate from what users expect, or what they consider fair, relative to their own context and experiences. In this exploratory work, we put forward the notion of transparency paths, a process by which we document our position, choices, and perceptions when developing and/or using algorithmic platforms. We conducted a self-reflection exercise with seven researchers, who collected and analyzed two sets of images; one depicting an everyday activity, “washing hands,” and a second depicting the concept of “home.” Participants had to document their process and choices, and in the end, compare their work to others. Finally, participants were asked to reflect on the definitions of bias and diversity. The exercise revealed the range of perspectives and approaches taken, underscoring the need for future work that will refine the transparency paths methodology.

References

  1. Ahmed Allam, Peter Johannes Schulz, and Kent Nakamoto. 2014. The impact of search engine selection and sorting criteria on vaccination beliefs and attitudes:Two experiments manipulating google output. Journal of Medical Internet Research 16, 4 (2014), e100. https://doi.org/10.2196/jmir.2642Google ScholarGoogle ScholarCross RefCross Ref
  2. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica, May 23(2016), 2016.Google ScholarGoogle Scholar
  3. Ricardo Baeza-Yates. 2018. Bias on the web. Commun. ACM 61, 6 (2018), 54–61. https://doi.org/10.1145/3209581Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, and Michael Hind. 2021. Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. In International Conference on Persuasive Technology. Springer, Cham. https://arxiv.org/abs/2101.12715Google ScholarGoogle Scholar
  5. Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, and Benjamin Timmermans. 2021. This Is Not What We Ordered : Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3404835.3462851Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Robert Epstein and Ronald E. Robertson. 2015. The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections. Proceedings of the National Academy of Sciences of the United States of America 112, 33 (2015), E4512–E4521. https://doi.org/10.1073/pnas.1419828112Google ScholarGoogle ScholarCross RefCross Ref
  7. F. Giunchiglia, Khuyagbaatar Batsuren, and Gabor Bella. 2017. Understanding and Exploiting Language Diversity.. In IJCAI. 4009–4017.Google ScholarGoogle Scholar
  8. F. Giunchiglia, K. Batsuren, and A. A. Freihat. 2018. One World–Seven Thousand Languages. In Proceedings 19th International Conference on Computational Linguistics and Intelligent Text Processing. 18–24.Google ScholarGoogle Scholar
  9. Fausto Giunchiglia and Mattia Fumagalli. 2020. Entity Type Recognition – dealing with the Diversity of Knowledge. In Knowledge Representation Conference (KR). Rhodes, Greece.Google ScholarGoogle ScholarCross RefCross Ref
  10. F. Giunchiglia, V. Maltese, and B. Dutta. 2012. Domains and context: first steps towards managing diversity in knowledge. Journal of Web Semantics, special issue on Reasoning with Context in the Semantic Web (2012), 53–63.Google ScholarGoogle Scholar
  11. Fausto Giunchiglia, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Veronika Bogina, Avital Shulner Tal, and Tsvi Kuflik. 2021. Diversity, Bias and Algorithmic Transparency. arXiv preprint arXiv:2104.05658(2021).Google ScholarGoogle Scholar
  12. G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. J. Miller. 1990. Introduction to WordNet: An on-line lexical database. International journal of lexicography 3, 4 (1990), 235–244.Google ScholarGoogle ScholarCross RefCross Ref
  13. Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju, Vasileios Iosifidis, Wolfgang Nejdl, Maria Esther Vidal, Salvatore Ruggieri, Franco Turini, Symeon Papadopoulos, Emmanouil Krasanakis, Ioannis Kompatsiaris, Katharina Kinder-Kurlanda, Claudia Wagner, Fariba Karimi, Miriam Fernandez, Harith Alani, Bettina Berendt, Tina Kruegel, Christian Heinze, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, and Steffen Staab. 2020. Bias in data-driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10, 3(2020), 1–14. https://doi.org/10.1002/widm.1356Google ScholarGoogle ScholarCross RefCross Ref
  14. William Thomas Parry and Edward Hacker. 1991. Aristotelian Logic. State University of New York Press.Google ScholarGoogle Scholar
  15. Frances A. Pogacar, Amira Ghenai, Mark D. Smucker, and Charles L.A. Clarke. 2017. The Positive and Negative Influence of Search Results on People’s Decisions about the Efficacy of Medical Treatments. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval(ICTIR ’17). Association for Computing Machinery, New York, NY, USA, 209–216. https://doi.org/10.1145/3121050.3121074Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Cornelius Puschmann. 2018. Beyond the Bubble: Assessing the Diversity of Political Search Results. Digital Journalism (2018), 1–20. https://doi.org/10.1080/21670811.2018.1539626Google ScholarGoogle Scholar
  17. David R Thomas. 2006. A general inductive approach for analyzing qualitative evaluation data. American journal of evaluation 27, 2 (2006), 237–246.Google ScholarGoogle Scholar
  18. Ryen W. White. 2013. Beliefs and biases in web search. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR ’13). Association for Computing Machinery, New York, NY, USA, 3–12. https://doi.org/10.1145/2484028.2484053Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Ryen W. White and Ahmed Hassan. 2014. Content bias in online health search. ACM Transactions on the Web 8, 4 (2014), 1–33. https://doi.org/10.1145/2663355Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Ryen W. White and Eric Horvitz. 2014. Belief dynamics in web search. Journal of the Association for Information Science and Technology 65, 11(2014), 2165–2178. https://doi.org/10.1002/asi.23128Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Transparency Paths - Documenting the Diversity of User Perceptions
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
      June 2021
      431 pages
      ISBN:9781450383677
      DOI:10.1145/3450614

      Copyright © 2021 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 June 2021

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate162of633submissions,26%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format