Dezembro 2021 vol. 9 num. 5 - 14th International Conference of the European Academy of Design, Safe Harbours for Design Research

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Data is the New Plastics: Developing Machine Learning UX Design Methods for Artificial Intelligence

Data is the New Plastics: Developing Machine Learning UX Design Methods for Artificial Intelligence

Krupa, Frédérique ;

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Designers’ next technological frontier is the creation of artificial intelligence (AI) for and within design systems. AI deployed in products, services and systems make use of Adrian Forty’s (1986) Suppressive design strategy to reduce consumer resistance to technological progress. AI production is currently the exclusive domain of data scientists and engineers, and a narrow form of AI is produced through machine learning (ML) algorithms that extract patterns from data in order to automate cognitive processes like predictions, categorizations, clustering, pathfinding, optimizations and more. ML’s homogenous production teams urgently needs diversifying , but machine learning’s algebraic and statistical foundations serve as intimidating gatekeepers to this complex universe. However, ML is simply the new frontier that designers must engage in — like other technical domains that preceded it. Designing ML-enhanced user experiences requires the development of new design pedagogy and research methods, hybridizing design with engineering and social sciences. The designer’s role on a production team, as well as the granularity and focus of a designer’s technical understanding, are currently up for debate. We proposes an approach called MLUX (Machine Learning User Experience), a syllabus for designing user experiences (UX) for ethical, responsible AI-based systems.

Full Paper:

Designers’ next technological frontier is the creation of artificial intelligence (AI) for and within design systems. AI deployed in products, services and systems make use of Adrian Forty’s (1986) Suppressive design strategy to reduce consumer resistance to technological progress. AI production is currently the exclusive domain of data scientists and engineers, and a narrow form of AI is produced through machine learning (ML) algorithms that extract patterns from data in order to automate cognitive processes like predictions, categorizations, clustering, pathfinding, optimizations and more. ML’s homogenous production teams urgently needs diversifying , but machine learning’s algebraic and statistical foundations serve as intimidating gatekeepers to this complex universe. However, ML is simply the new frontier that designers must engage in — like other technical domains that preceded it. Designing ML-enhanced user experiences requires the development of new design pedagogy and research methods, hybridizing design with engineering and social sciences. The designer’s role on a production team, as well as the granularity and focus of a designer’s technical understanding, are currently up for debate. We proposes an approach called MLUX (Machine Learning User Experience), a syllabus for designing user experiences (UX) for ethical, responsible AI-based systems.

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Palavras-chave: MLUX, machine Learning user experience, artificial intelligence design, data science, suppressive design,

Palavras-chave: MLUX, machine Learning user experience, artificial intelligence design, data science, suppressive design,

DOI: 10.5151/ead2021-180

Referências bibliográficas
  • [1] "ACM FAccT (2020). ACM Conference on Fairness, Accountability, and Transparency. Retrieved April 30, 2021: https://facctconference.org/index.html
  • [2] Alkhatib, A. (2021, May). To live in their utopia: Why algorithmic systems create absurd outcomes. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-9). Transcript of presentation retrieved 15/05/2021: https://t.co/zgM6E45nuz?amp=1
  • [3] Carney, M. (2018) Designing machine learning. Stanford d.School (2018-2019). Course website consulted on 3/2/2020: https://designwith.ml/
  • [4] Carney, M. (2019) Designing machine learning. Stanford d.School (2019-2020). Course website consulted on 3/2/2020: bit.ly/designingML2020
  • [5] Chiusi, F. & al. (Eds.) (2020). Automating Society Report 2020. Algorithm Watch, Bertelsmann Siftung. Retrieved April 30, 2021: https://automatingsociety.algorithmwatch.org
  • [6] Chollet, F. (2020). https://twitter.com/fchollet/status/1214392496375025664 published on 07/01/2020.
  • [7] Costanza-Chock, S. (2020). Design justice: Community-led practices to build the worlds we need. The MIT Press.
  • [8] Costanza-Chock, S. And Philip, N. (2018). Design justice, AI, and escape from the matrix of domination. Journal of Design and Science. 3(5). Retrieved 15/05/2021: https://jods.mitpress.mit.edu/pub/costanza-chock/release/4
  • [9] Dove, G., Halskov, K., Forlizzi, J., & Zimmerman, J. (2017, May). UX design innovation: Challenges for working with machine learning as a design material. In Proceedings of the 2017 chi conference on human factors in computing systems (pp. 278-288).
  • [10] Forty, A. (1986). Objects of desire: Design and society since 1750. Thames and Hudson.
  • [11] IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, First Edition. Retrieved May 15, 2021: https://standards.ieee.org/content/ieee-standards/en/industry-connections/ec/ autonomous-systems.html
  • [12] Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
  • [13] Krupa, F. (2018). Girl games: gender, design and technology in the service of women’s recruitment in ICT?. PhD dissertation, the Université Paris 1 Panthéon-Sorbonne, 30/11/2018.
  • [14] Krupa, F. (2020). The designer in the AI/machine learning creation process. Proceedings of CUMULUS ROMA 2021. June 9, 2021.
  • [15] Krupa, F. & al. (2020). L’UX Design: Livret des Méthodes. L’école de design Nantes Atlantique. https://www.lecolededesign.com/fichier/p_paralien/6610/livre_ux_digital_design_lab.pdf
  • [16] Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501-507.
  • [17] Norman, D. A. (2004). Emotional design: Why we love (or hate) everyday things. Basic Civitas Books.
  • [18] Picard, R. W. (2000). Affective computing. MIT press.
  • [19] The Economist (2017). The world’s most valuable resource is no longer oil, but data. The Economist. May 6, 2017 online edition: https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data.
  • [20] Wachter-Boettcher, S. (2017). Technically wrong: Sexist apps, biased algorithms, and other threats of toxic tech. WW Norton & Company.
  • [21] Winner, L. (1980). Do Artifacts have politics? Daedalus: Modern Technology: Problem or Opportunity?, 109(1), Winter 1980, 121-136.
  • [22] Yang, Q., Scuito, A., Zimmerman, J., Forlizzi, J., & Steinfeld, A. (2018a, June). Investigating how experienced UX designers effectively work with machine learning. In Proceedings of the 2018 Designing Interactive Systems Conference (pp. 585-596).
  • [23] Yang, Q., Banovic, N., & Zimmerman, J. (2018b, April). Mapping machine learning advances from hci research to reveal starting places for design innovation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-11).
  • [24] Yang, Q. (2018c, March). Machine learning as a UX design material: How can we imagine beyond automation, recommenders, and reminders?. In AAAI Spring Symposia.
  • [25] "
Como citar:

Krupa, Frédérique; "Data is the New Plastics: Developing Machine Learning UX Design Methods for Artificial Intelligence", p. 447-456 . In: 14th International Conference of the European Academy of Design, Safe Harbours for Design Research. São Paulo: Blucher, 2021.
ISSN 2318-6968, DOI 10.5151/ead2021-180

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