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Fausto Giunchiglia Styliani Kleanthous, Jahna Otterbacher Tim Draws Transparency Paths - Documenting the Diversity of User Perceptions Inproceedings Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, 2021, ISBN: 9781450383677. Abstract | Links | BibTeX | Tags: Algorithmic Transparency, Diversity @inproceedings{Giunchiglia2021, title = {Transparency Paths - Documenting the Diversity of User Perceptions}, author = {Fausto Giunchiglia, Styliani Kleanthous, Jahna Otterbacher, Tim Draws}, url = {https://dl.acm.org/doi/abs/10.1145/3450614.3463292}, doi = {10.1145/3450614.3463292}, isbn = {9781450383677}, year = {2021}, date = {2021-06-21}, booktitle = {Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization}, publisher = {Association for Computing Machinery}, series = {UMAP '21}, 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.}, keywords = {Algorithmic Transparency, Diversity}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
Fausto Giunchiglia Jahna Otterbacher, Styliani Kleanthous Khuyagbaatar Batsuren Veronika Bogin Tsvi Kuflik Avital Shulner Tal Towards Algorithmic Transparency: A Diversity Perspective Journal Article arXiv preprint arXiv:2104.05658, 2021. Abstract | Links | BibTeX | Tags: Algorithmic Transparency, Diversity @article{Giunchiglia2021b, title = {Towards Algorithmic Transparency: A Diversity Perspective}, author = {Fausto Giunchiglia, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Veronika Bogin, Tsvi Kuflik, Avital Shulner Tal}, url = {https://arxiv.org/abs/2104.05658}, year = {2021}, date = {2021-04-12}, journal = {arXiv preprint arXiv:2104.05658}, abstract = {As the role of algorithmic systems and processes increases in society, so does the risk of bias, which can result in discrimination against individuals and social groups. Research on algorithmic bias has exploded in recent years, highlighting both the problems of bias, and the potential solutions, in terms of algorithmic transparency (AT). Transparency is important for facilitating fairness management as well as explainability in algorithms; however, the concept of diversity, and its relationship to bias and transparency, has been largely left out of the discussion. We reflect on the relationship between diversity and bias, arguing that diversity drives the need for transparency. Using a perspective-taking lens, which takes diversity as a given, we propose a conceptual framework to characterize the problem and solution spaces of AT, to aid its application in algorithmic systems. Example cases from three research domains are described using our framework.}, keywords = {Algorithmic Transparency, Diversity}, pubstate = {published}, tppubtype = {article} } As the role of algorithmic systems and processes increases in society, so does the risk of bias, which can result in discrimination against individuals and social groups. Research on algorithmic bias has exploded in recent years, highlighting both the problems of bias, and the potential solutions, in terms of algorithmic transparency (AT). Transparency is important for facilitating fairness management as well as explainability in algorithms; however, the concept of diversity, and its relationship to bias and transparency, has been largely left out of the discussion. We reflect on the relationship between diversity and bias, arguing that diversity drives the need for transparency. Using a perspective-taking lens, which takes diversity as a given, we propose a conceptual framework to characterize the problem and solution spaces of AT, to aid its application in algorithmic systems. Example cases from three research domains are described using our framework. |