and techniques are everybody’s
material for creating better digital
experiences.
Endnotes
1. https://aaai.org/Symposia/symposia.php
2. Although we could only select a few of the
papers for this Special Topic, we want to
acknowledge that the ideas in this short
introduction were developed with all the
participants of the 2017 and 2018 symposia.
3. See Lars Holmquist’s article in
Interactions: Holmiquist, L. Intelligence
on tap: AI as a new design material.
Interactions 24, 4 (2017); http://
interactions.acm.org/archive/view/july-
august-2017/intelligence-on-tap
4. On Explainable AI: https://en.wikipedia.
org/wiki/Explainable_Artificial_
Intelligence
Elizabeth Churchill is a director of user
experience at Google. Her current research
focuses on effective tools for creative work,
including tools for interactive technology
designers and developers. She is the current
vice president of the ACM.
→ churchill@acm.org
Philip van Allen is a professor at ArtCenter
College of Design, interested in new models for
the IxD of AI, including non-anthropomorphic
animistic design. He also develops tools
for prototyping complex technologies and
consults for industry. He received his B. A. in
experimental psychology from University of
California, Santa Cruz.
→ vanallen@artcenter.edu
Mike Kuniavsky is a user experience designer,
researcher, and author. Currently at PARC, he
previously cofounded several successful user-experience-centered companies, including
ThingM, which designs and manufactures
ubiquitous computing and Internet of Things
products, and Adaptive Path, a well-known
design consultancy.
→ mike.kuniavsky@parc.com
he recently developed and is refining
with his students. The tool is itself
a prototype that asks the questions:
What should an AI prototyping tool
look like? What affordances do AI
designers need? What are the new
requirements for the design of AI?
• AI collaborators. Rather than
seeing AI as a way to automate
activities or provide solutions,
AI systems can be designed as
collaborators that participate
with humans in creating shared
outcomes. In this sense, human
beings and AI approaches augment
each other. This takes into account
concepts around cybernetics,
distributed cognition, the limits of
narrow AI, and the complexity of
human creativity. “From Machine
Learning to Machine Teaching:
The Importance of UX” by Martin
Lindvall, Jesper Molin, and Jonas
Löwgren discusses this cooperative
approach. The authors address
human-machine teaching as a
key factor in building effective
machine-learning systems, pointing
out that learning algorithms can
offer far superior predictions when
they have substantial amounts
of high-quality, well-annotated
training data. They describe a two-step process for involving people in
the labeling of training data as part
of their everyday tasks, offering
examples from the analysis of
medical images.
• Explainable AI (XAI) [ 4]. Beyond
the technical challenges of XAI, our
discussion focused on the design
issues involved. What affordances
can we make available so the user
can respond to explanations? How
much explanation is too much?
What is the role of trust? What
if an AI decision is non-intuitive?
Does the public need to learn the
character of how AI makes decisions?
Cramer et al.’s article, “Assessing
and Addressing Algorithmic Bias in
Practice,” explores one critical facet
of XAI in considering algorithmic
bias and calling to make AI systems
more visible, transparent, and
interrogable.
The symposia confirmed our
view that discussions that bridge
HCI concerns, design research and
practice, and AI have been lacking—
but there is a great deal of positive
energy around the idea that we
could build strong communities of
collaboration and practice to address
this lack. Our discussions clearly
point to the potential and value of
treating AI techniques, the data they
utilize, and the results they produce
as design materials worthy of critical
reflection and investigation. They
also ask us to consider the many
levels of granularity and scopes
of impact, whether what is being
designed is a micro interaction, an
urban infrastructure, or a social
structure.
We hope that the articles in this
Special Topic spark some broader
discussions and ideation in the HCI
and IxD communities, and that
further symposia and workshops
will reinforce that AI methods
DOI: 10.1145/3281764 © 2018 ACM 1072-5520/18/11 $15.00
