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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Tue, 12 Feb 2019 14:17:21 -0600
Haiyi Zhu <[log in to unmask]>
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Haiyi Zhu <[log in to unmask]>
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Dear CHI members,

If you are doing research at the intersection of HCI and AI, we would
encourage you to submit to a Special Issue of the HCI Journal,
<> "Unifying Human Computer Interaction and
Artificial Intelligence". You can find more information at

*Timeline *
March 20, 2019: Proposals Due (1,000 words)
June 15, 2019: Full Papers Due


*Full CfP:*
Special Issue on *Unifying Human Computer Interaction and Artificial

Special Issue Editors:
Munmun De Choudhury, Min Kyung Lee, David A. Shamma, and Haiyi Zhu

Over the past decade, artificial intelligence (AI) has increasingly been
deployed across many domains such as transportation, retail, criminal
justice, finance and health. But these very domains that AI is aiming to
revolutionize may also be where human implications are the most momentous.
The potential negative effects of AI on society, whether amplifying human
biases or the perils of automation, cannot be ignored and as a result such
topics are increasingly discussed in scholarly and popular press contexts.
As the New York Times notes: “[...] if we want [AI] to play a positive role
in tomorrow’s world, it must be guided by human concerns.”
However, simply introducing human guidance or human sensitivity into AI is
not going to be enough to realize AI’s full potential or to prevent its
unintended consequences. AI is increasingly being incorporated into
technology design, including technologies of deep interest to researchers
and practitioners in human computer interaction (HCI). While most AI-based
approaches offer promising methods for tackling real-world problems, many
of the technologies they enable have been developed in isolation, without
appropriate involvement of the human stakeholders who use these systems and
who are the most affected by them. Human involvement in AI system design,
development, and evaluation is critical to ensure that AI-based systems are
practical, with their outputs being meaningful and relatable to those who
use them. Moreover, human activities and behaviors are deeply contextual,
complex, nuanced, and laden with subjectivity; aspects which may cause
current AI-based approaches to fail as they cannot adequately addressed by
simply adding more data. As a result, to ensure the success of future
AI approaches,
we must incorporate new complementary human-centered insights. These
include stakeholders’ demands, beliefs, values, expectations, and
preferences—attributes that constitute a focal point of HCI research—and
which need to be a part of the development of these AI-based technologies.
The same issues also give rise to important new methodological questions.
For instance, how can existing HCI methodology incorporate AI methods and
data to develop intelligent systems to improve the human condition? What
are the best ways to bridge the gap between machines and humans while
designing technologies? How can AI enhance the human experience in
interactive technologies; and further could it help define new styles of
interaction? How will conventional evaluation techniques in HCI need to be
modified in contexts where AI is a core technology component? What existing
research methods might be most compatible with AI approaches? And, what
will be involved in training the next generation of HCI researchers who
want to work at the intersection with AI? Of course the concepts of
“design”, “interaction”, and “evaluation” continue to be interpreted by
different HCI researchers and practitioners in many related but
non-identical ways. Nonetheless, how the potential synergy between AI and
HCI will influence these interpretations remains an open but pertinent

Naturally, conversations about the relationship between HCI and AI are not
new. Shneiderman and Maes (1997) discussed if AI should be a primary
metaphor in the human interface to computers. Similarly, Grudin (2009)
described alternating cycles in which one approach flourished, while the
other suffered a “winter”, characterized by a period of reduced funding,
and academic and popular interest. And more than a decade ago, Winograd
(2006) argued about the strengths and limitations, as well as the relevance
of rationalistic and design approaches offered by AI and HCI respectively,
when applied to “messy” human problems. While the landscape of both AI and
HCI research has significantly evolved since these early conversations, and
researchers have begun to be more vocal about the need for a stronger
“marriage” between HCI and AI, nevertheless the competing philosophies and
research styles of the two fields, the current context, both academic and
societal, demands renewed attention to unifying HCI and AI.
This special issue aims to be a step forward in this regard. We hope to
revive and extend prior attempts to bridge HCI and AI, given the burgeoning
promise and traction AI has invited recently in tackling challenging human
problems. In doing so, we seek to engage both HCI and AI researchers
contributing theoretical, empirical, systems, or design papers that aim to
unify these two perspectives. We want to bring together research that spans
this wide set of issues to help integrate the different parts of this
emerging space. By doing so, we aim to begin a constructive dialog to
bridge the gap via original research.

Submissions should address key questions in unifying AI and HCI. The
following questions are intended to be inspiring, not limiting:

   - How can we address the socio-technical challenges in AI development
   involving ethical considerations, such as biases, fairness, privacy, equity
   and diversity?
   - How can we bridge the fundamental mismatch between human-styles of
   interpretation, reasoning, and feedback and the machine’s statistical
   optimization for data with high-dimensionality?
   - How can we incorporate human insights—including stakeholders’ demands,
   beliefs, values, expectations, and preferences—into the development of AI
   - How can we predict the societal consequences of AI system deployment?
   - How can we systematically evaluate the social, psychological, and
   economic impacts of AI technologies?
   - How can we train our next-generation developers and designers to
   create AI system in a human-centered manner?
   - How does AI change how we design and prototype new HCI systems and
   - How should AI interactions be designed to help end users understand AI and
   make better decisions?
   - What HCI methods can we use to address AI’s limitations?
   - What design methods and prototyping tools can help us create
novel AI applications
   and services?
   - How might existing human-centric methods help increase algorithmic
   transparency and explainability?
   - Where can AI help HCI in testing, evaluation, and User Experience ?

*Proposed Timeline *
March 20, 2019: Proposals Due
April 8, 2019: Response to Authors Due
June 15, 2019: Full Papers Due
September 1, 2019: Reviews to Authors
November 8, 2019: Revised Papers Due
January 17, 2020: Reviews to Authors Due
February 21, 2020: Final Papers Due

*Submission of Proposals*
To help authors find a good fit, we will solicit proposals. Proposals
should be about 1000 words and provide a clear indication of what the paper
is about. Given the relatively short publication cycle we will favor
research that is relatively mature. Note that you must use the template
provided on the journal website (available here
<> or here
Proposals will be evaluated for relevance to the special issue theme, and
feedback will be given. Both proposal and full paper submissions should be
submitted to the HCI Editorial site (

Follow the guidelines and instructions for submissions on the site. There
is a place on the submission site to note that your submission is for the
specialissue. Full paper Special Issue submissions will be peer reviewed to
the usual standards of the HCI journal.

For questions about the special issue, please send mail to si.hci
[log in to unmask]

*Guest Editors*
Munmun De Choudhury (Georgia Tech)
Min Kyung Lee (Carnegie Mellon University)
David A. Shamma (FXPAL)
Haiyi Zhu (University of Minnesota)

Haiyi Zhu, Ph.D.
Assistant Professor
Department of Computer Science and Engineering
University of Minnesota

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