Social Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child

Social Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child

Abstract

AI technologies are often used to aid people in performing discrete tasks with well-defined goals (e.g., recognising faces in images). Emerging technologies that provide continuous, real-time information enable more open-ended AI experiences. In partnership with a blind child, we explore the challenges and opportunities of designing human-AI interaction for a system intended to support social sensemaking. Adopting a research-through-design perspective, we reflect upon working with the uncertain capabilities of AI systems in the design of this experience. We contribute: (i) a concrete example of an open-ended AI system that enabled a blind child to extend his own capabilities; (ii) an illustration of the delta between imagined and actual use, highlighting how capabilities derive from the human-AI interaction and not the AI system alone; and (iii) a discussion of design choices to craft an ongoing human-AI interaction that addresses the challenge of uncertain outputs of AI systems.

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Authors
  • Morrison, Cecily
  • Cutrell, Edward
  • Grayson, Martin
  • Thieme, Anja
  • Taylor, Alex S.
  • Roumen, Geert
  • Longden, Camilla
  • Tschiatschek, Sebastian
  • Marques, Rita Faia
  • Sellen, Abigail
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
Conference on Human Factors in Computing Systems (CHI 2021)
Divisions
Data Mining and Machine Learning
Event Location
Yokohama, Japan - virtual
Event Type
Conference
Event Dates
08.-13.05.2021
Series Name
CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
ISSN/ISBN
978-1-4503-8096-6
Publisher
ACM
Page Range
396:1-396:14
Date
8 May 2021
Official URL
https://doi.org/10.1145/3411764.3445290
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