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Dear Colleagues,
Please consider submitting to HICSS 2021: Explainable Artificial
Intelligence (XAI) mini track (
https://hicss.hawaii.edu/tracks-54/decision-analytics-and-service-science/#explainable-artificial-intelligence-xai-minitrack
).
Fast track journal opportunity at *Information Systems Frontiers *
*Description*:
The use of Artificial Intelligence (AI) in the context of decision
analytics and service science has received significant attention
in
academia and practice alike. Yet, much of the current efforts have
focused
on advancing underlying algorithms and not on decreasing the
complexity of
AI systems. AI systems are still “black boxes” that are difficult
to
comprehend—not only for developers, but particularly for users and
decision-makers. Also, the development and use of AI is associated
with
many risks and pitfalls like biases in data or predictions based
on
spurious correlations (“Clever Hans” phenomena), which eventually
may lead
to malfunctioning or biased AI and hence technologically driven
discrimination.
This is where research on Explainable Artificial Intelligence
(XAI) comes
in. Also referred to as “transparent,” “interpretable,” or
“understandable
AI,” XAI aims to “produce explainable models, while maintaining a
high
level of learning performance (prediction accuracy); and enable
human users
to understand, appropriately, trust, and effectively manage the
emerging
generation of artificially intelligent partners”. XAI hence refers
to “the
movement, initiatives, and efforts made in response to AI
transparency and
trust concerns, more than to a formal technical concept”,
eventually
impacting task performance of users.
With a focus on decision support, this minitrack aims to explore
and extend
research on how to establish explainability of intelligent black
box
systems—machine learning-based or not. We especially look for
contributions
that investigate XAI from either a developer’s or user’s
perspective. We
invite submissions from all application domains, such as
healthcare,
finance, e-commerce, retail, public administration or others.
Technically
and method-oriented studies as well as design science or
behavioral science
approaches are welcome.
*Topics of interest include, but are not limited to:*
- *The developers’ perspective on XAI*
- XAI to open, control and evaluate black box algorithms
- Using XAI to identify bias in data
- Explainability and Human-in-the-Loop development of AI
- XAI to support interactive machine learning
- Prevention and detection of deceptive AI explanations
- XAI to discover deep knowledge and learn from AI
- *The users’ perspective on XAI*
- Presentation and personalization of AI explanations for
different
target groups
- XAI to increase situational awareness, compliance behavior and
task
performance
- XAI for transparency and unbiased decision making
- Impact of explainability on AI-based decision support systems
use
and adoption
- Explainability of AI in crisis situations
- Potential harm of explainability in AI
We provide the opportunity for (extended) best papers of this
minitrack to
be fast-tracked to the Information Systems Frontiers (ISF)
journal.
Important Dates for Paper Submission
June 15, 2020: Paper Submission Deadline (11:59 pm HST)
August 17, 2020: Notification of Acceptance/Rejection
Minitrack Co-Chairs:
Christian Meske (Primary Contact)
Freie Universität Berlin and Einstein Center Digital Future
christian.meske@fu-berlin.de
Babak Abedin
University of Technology Sydney
Babak.Abedin@uts.edu.au
Iris Junglas
College of Charleston
junglasia@cofc.edu
Fethi Rabhi
University of New South Wales
f.rabhi@unsw.edu.au
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