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Transparency Paths - Documenting the Diversity of User Perceptions

Published: 22 June 2021 Publication 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.

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  • (2022)Mitigating Bias in Algorithmic Systems—A Fish-eye ViewACM Computing Surveys10.1145/352715255:5(1-37)Online publication date: 3-Dec-2022

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    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
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    Published: 22 June 2021

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    • (2022)Mitigating Bias in Algorithmic Systems—A Fish-eye ViewACM Computing Surveys10.1145/352715255:5(1-37)Online publication date: 3-Dec-2022

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