Reflexive Data Curation: Opportunities and Challenges for Embracing Uncertainty in Human–AI Collaboration

Anne Arzberger*, Maria Luce Lupetti, Elisa Giaccardi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

This article presents findings from a Research through Design investigation focusing on a reflexive approach to data curation and the use of generative AI in design and creative practices. Using binary gender categories manifested in children’s toys as a context, we examine three design experiments aimed at probing how designers can cultivate a reflexive human-AI practice to confront and challenge their internalized biases. Our goal is to underscore the intricate interplay between the designer, AI technology, and publicly held imaginaries and to offer an initial set of tactics for how personal biases and societal norms can be illuminated through interactions with AI. We conclude by proposing that designers not only bear the responsibility of grappling critically with the complexities of AI but also possess the opportunity to creatively harness the limitations of technology to craft a reflexive data curation that encourages profound reflections and awareness within design processes.
Original languageEnglish
Article number74
Number of pages33
JournalACM Transactions on Computer-Human Interaction
Volume31
Issue number6
DOIs
Publication statusPublished - 2024

Keywords

  • human-AI collaboration
  • human-AI reflexive practices
  • machine-learning uncertainty
  • reflexive data curation
  • research through design

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