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Data4Good: Designing for Diversity and Development

Published: 02 October 2020 Publication History

Abstract

We are witnessing unprecedented datafication of the society we live in, alongside rapid advances in the fields of Artificial Intelligence and Machine Learning. However, emergent data-driven applications are systematically discriminating against many diverse populations. A major driver of the bias are the data, which typically align with predominantly Western definitions and lack representation from multilingually diverse and resource-constrained regions across the world. Therefore, data-driven approaches can benefit from integration of a more human-centred orientation before being used to inform the design, deployment, and evaluation of technologies in various contexts. This workshop seeks to advance these and similar conversations, by inviting researchers and practitioners in interdisciplinary domains to engage in conversation around how appropriate human-centred design can contribute to addressing data-related challenges among marginalised and under-represented/underserved groups.

References

[1]
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE. https://doi.org/10.1109/cvpr.2009.5206848
[2]
James Zou and Londa Schiebinger. 2018. AI can be sexist and racist --- it's time to make it fair. Nature 559, 7714 (jul 2018), 324--326. https://doi.org/10.1038/d41586-018-05707-8

Cited By

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  • (2023)Data Practice for a Politics of CareProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580831(1-13)Online publication date: 19-Apr-2023
  • (2022)Data4Good: An Established Framework for supporting Civil Society Organizations2022 IEEE International Humanitarian Technology Conference (IHTC)10.1109/IHTC56573.2022.9998401(73-78)Online publication date: 2-Dec-2022

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  1. Data4Good: Designing for Diversity and Development

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      cover image ACM Other conferences
      AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces
      September 2020
      613 pages
      ISBN:9781450375351
      DOI:10.1145/3399715
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 02 October 2020

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      Author Tags

      1. AI for social good
      2. data literacies
      3. diversity
      4. interdisciplinary computing
      5. multilingual/multicultural contexts

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      • Extended-abstract
      • Research
      • Refereed limited

      Conference

      AVI '20
      AVI '20: International Conference on Advanced Visual Interfaces
      September 28 - October 2, 2020
      Salerno, Italy

      Acceptance Rates

      AVI '20 Paper Acceptance Rate 36 of 123 submissions, 29%;
      Overall Acceptance Rate 128 of 490 submissions, 26%

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      Cited By

      View all
      • (2023)Data Practice for a Politics of CareProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580831(1-13)Online publication date: 19-Apr-2023
      • (2022)Data4Good: An Established Framework for supporting Civil Society Organizations2022 IEEE International Humanitarian Technology Conference (IHTC)10.1109/IHTC56573.2022.9998401(73-78)Online publication date: 2-Dec-2022

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