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Finally, there’s the human. Computational interaction can be viewed as a continuation
of the long history of automation, which has undergone a series of victories and setbacks
due to complexity causing the loss of agency, deskilling, demotivation, and so on. Once a
computational interaction technique is operating there is a risk the user is losing agency as
a consequence of an overly complex system aiding or overriding the user. Such problems
are exacerbated when the system fails to correctly infer the user’s intention, in particular,
if the system fails in an unexpected way, or if it fails to oer suitable controls and inter-
pretability. Computational interaction should oer appropriate degree of transparency that
allowsuserstoatunderstandthemechanismsleadingtoaparticularsystemprediction
or suggestion at such a level that they can achieve their goals. To do so eectively either
requires understanding users’ existing workows and practices, users to adapt to new ways
of interaction tailed for computational interaction design, or a combination of both. From
the perspective of algorithms, even the design problem is centered around mathematics:
the central problem for user interface design for algorithmic systems is to assist users
in understanding and shaping control, learning, and optimization functions and guiding
a system-informed exploration of the decision space. What is the best user interface to
mathematics? When this problem is successfully solved, computers can support users’
creativity by assisting them in eectively exploring high-dimensional decision spaces. By
modelling a domain-specic creative process, it is possible to optimize the creative process
itself and help identify suitable solutions that are better according to some criteria, such as
speed, aesthetics, novelty, etc. These challenges call for collaboration with designers and
design researchers.
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