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Bran Knowles ( firstname.lastname@example.org) is a lecturer
in data science at Lancaster University, Lancaster, U. K.
Alison Smith-Renner ( email@example.com) leads
the Machine Learning Visualization Lab at Decisive
Analytics Corporation, Arlington, VA, USA, and is
a Ph.D. candidate in computer science at the University
of Maryland, College Park, MD, USA.
Forough Poursabzi-Sangdeh (forough.poursabzi@
microsoft.com) is a post-doctoral researcher at Microsoft
Research NYC, USA.
Di Lu ( firstname.lastname@example.org) is a Ph. D. student in the School
of Information Sciences at the University of Pittsburgh,
Pittsburgh, PA, USA.
Halimat Alabi ( email@example.com) is an adjunct in the Art
Institute Online and a Ph.D. candidate in the School
of Interactive Art and Technology at Simon Fraser
University, Vancouver, BC, Canada.
© 2018 ACM 0001-0782/18/12 $15.00
that data from individuals’ self-tracking devices will be used as bedrock
data in other systems from which a
range of inferences are made. Ensuring uncertainties are preserved and
communicated throughout a long
chain of systems whose developers
and interpreters might have different
readings of these uncertainties and
tolerances for them is challenging
but necessary if the systems are to be
interpretable at scale, as discussed by
Meyer et al. 19 This requires development of mechanisms for ensuring important context is not lost, including,
say, both the uncertainties and uncertainty tolerances at different points
along the chain.
How to tailor communication of uncertainties. Designs must be flexible
and/or customizable, presenting uncertainty information in ways that are
understandable by the full range of
end users with differing needs in data
granularity and information presentation. Given that much of the value of
health wearables for lay consumers
comes from data being available at-a-glance, there is a need to balance
important nuance with the interface
usability, as discussed by Liu et al. 16
Still, there are moments when even
lay consumers could require access
to uncertainty information, with systems perhaps allowing them to delve
more deeply, as required. Contextual
information (such as users’ intended
and ongoing use of their wearable
data) might also be useful for determining what kinds of uncertainty information the system ought to communicate. At the same time, designers
must be cognizant of user variability
in cognitive and affective responses
to uncertainty-related information to
design systems that can identify, learn
from, and adapt to these responses to
inform health-related decision making most effectively.
These design implications can be
considered an open challenge to the
health-wearables community without
suggesting precise mechanisms for realizing them through design.
This article resulted from group work
that was part of the CHI 2017 work-
shop “Designing for Uncertainty in
HCI: When Does Uncertainty Help?”;
We wish to thank our fellow work-
shop participants and keynote speaker,
Susan Joslyn, for their feedback in de-
veloping these ideas. And in particular
we thank the workshop’s organizers—
Miriam Greis, Jessica Hullman, Mi-
chael Correll, Matthew Kay, and Orit
Shaer—for providing a forum for dis-
cussing these ideas and bringing this
team of authors together for ongoing
collaboration post workshop.
Finally, we also thank the anonymous reviewers for their help shaping
and improving this article.
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