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.
- 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 ScholarCross Ref
- Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica, May 23(2016), 2016.Google Scholar
- Ricardo Baeza-Yates. 2018. Bias on the web. Commun. ACM 61, 6 (2018), 54–61. https://doi.org/10.1145/3209581Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- F. Giunchiglia, Khuyagbaatar Batsuren, and Gabor Bella. 2017. Understanding and Exploiting Language Diversity.. In IJCAI. 4009–4017.Google Scholar
- 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 Scholar
- Fausto Giunchiglia and Mattia Fumagalli. 2020. Entity Type Recognition – dealing with the Diversity of Knowledge. In Knowledge Representation Conference (KR). Rhodes, Greece.Google ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- William Thomas Parry and Edward Hacker. 1991. Aristotelian Logic. State University of New York Press.Google Scholar
- 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 ScholarDigital Library
- 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 Scholar
- David R Thomas. 2006. A general inductive approach for analyzing qualitative evaluation data. American journal of evaluation 27, 2 (2006), 237–246.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
Index Terms
- Transparency Paths - Documenting the Diversity of User Perceptions
Recommendations
Transparency and reflection in distributed systems
EW 5: Proceedings of the 5th workshop on ACM SIGOPS European workshop: Models and paradigms for distributed systems structuringThe recursive composition of systems to form functionally equivalent transparently distributed systems is an important paradigm for constructing distributed systems. The extent to which such recursive transparency can be achieved depends crucially on ...
Perceptions of Computer System Usefulness: Insights for Design from Experienced Older Users
Proceedings, Part I, of the First International Conference on Human Aspects of IT for the Aged Population. Design for Aging - Volume 9193Computer systems have the potential to assist older adults by supporting independence, enhancing social communication, and enabling healthcare activities. Yet older adults' adoption rates continue to lag behind younger and middle-aged adults. We report ...
User Perceptions of Smart Home IoT Privacy
Smart home Internet of Things (IoT) devices are rapidly increasing in popularity, with more households including Internet-connected devices that continuously monitor user activities. In this study, we conduct eleven semi-structured interviews with smart ...
Comments