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ACM IUI Workshop -- HUMANIZE 2023 FIRST CALL FOR PAPERS
The 7th International Workshop on Transparency and Explainability
in
Adaptive Systems through User Modeling Grounded in Psychological
Theory
(HUMANIZE), in conjunction with the 28th ACM Conference on
Intelligent
User Interfaces (ACM IUI 2023), Sydney, Australia, 17-31 March
2023
Full details are available online:
http://www.humanize-workshop.org/
# IMPORTANT DATES
- Submission Deadline: 02 January 2023
- Notification to Authors: 29 January 2023
- Camera-ready: 08 February 2023
# MOTIVATION AND GOALS
More and more systems are designed to be intelligent; By relying
on data
and the application of machine learning, these systems adapt
themselves to
match predicted or inferred user needs, preferences.
Observable, measurable, objective interaction behavior plays a
central
role in the design of these systems, in both the predictive
modeling that
provides intelligence (e.g., predicting what web pages a website
visitor
will visit based on their historic navigation behavior) and the
evaluation
(e.g., decide if a system performs well based on the extent that
predictions are accurate and used correctly).
When designing more conventional systems (following approaches
such as
user-centered design or design thinking), designers rely on latent
user
characteristics (such as beliefs and attitudes, proficiency
levels,
expertise, personality) aside from objective, observable behavior.
By
relying on qualitative studies (e.g., observations, focus groups,
interviews) they consider not only user characteristics or
behavior in
isolation, but also the relationship among them. This combination
provides
valuable information on how to design the systems.
HUMANIZE aims to investigate the potential of combining the
quantitative,
data-driven approaches with the qualitative, theory-driven
approaches. We
solicit work from researchers that incorporate variables grounded
in
psychological theory into their adaptive/intelligent systems.
These
variables allow for designing adaptive systems from a more
user-centered
approach in terms of requirements or needs based on user
characteristics
rather than solely interaction behavior, which allows for:
Explainability
Any adaptive system that relies solely on the interaction behavior
data
can be explained in terms of expectations, perceptions, variables
and
models used from theory and define the users as entities, their
thinking
and feeling, while undertaking purposeful actions (and reactions)
regarding e.g., learning, reasoning, problem solving, decision
making.
Fairness
Any adaptive system that considers a human-centred model in its
core may
consider and respect the individual differences, enabling the
design and
creation of environments, interventions and AI algorithms that are
ethical, open to diversity, policies and legal challenges, and
treating
all users with fairness regarding their skills and unique
characteristics.
Transparency
Any adaptive system that utilizes the full potential of its
human-centred
model in terms of definition and impact on decisions made by AI
algorithms
may facilitate the visibility and transparency of the subsequent
actions
bringing the control back to the users, for regulating, monitoring
and
understanding an adaptive outcome that directly affects them.
Bias
Any adaptive system's AI algorithms and adaptive processes which
are
designed and developed considering human-centred model
characteristics,
the impact and relationships of subsequent variables, may
facilitate
informed interpretations and unveil possible bias decisions,
actions and
operations of users during their multi-purpose interactions.
# TOPICS OF INTEREST
A non-exhaustive list of topics for this workshop is:
- Identifying theory (e.g., personality, level of domain
knowledge,
cognitive styles) that can be used for user models for
personalizing user
interfaces.
- Investigating the impact of incorporating psychological theory
on
explainability, fairness, transparency, and bias
- Modeling for inferring of user variables from
observable/measureable/objective data (e.g., how to infer
personality from
social media, how to infer level of domain knowledge from
clickstreams).
- Designing better adaptive systems from inferred user variables
(e.g.,
altering the number of search results, ordering of interface
elements,
visual versus textual representations).
- User studies investigating one or more of the aspects mentioned
above.
# TYPES OF PAPERS
For this workshop we encourage three kinds of submissions:
- Full papers (anonymized 6-8 pages)
- Short papers (anonymized up to 4-6 pages)
- White papers/Position Statements (anonymized up to 2-4 pages)
* page count is excluding references
Submissions should follow the standard SigCHI format via the new
ACM
workflow. Use either the Microsoft Word template or the LaTeX
template:
https://www.acm.org/publications/taps/word-template-workflow
# SUBMISSION & PUBLICATION
All submissions will undergo a peer-review process to ensure a
high
standard of quality. Referees will consider originality,
significance,
technical soundness, clarity of exposition, and relevance to the
workshop's topics. The reviewing process will be double-blind so
submissions should be properly anonymized.
Research papers should be submitted electronically as a single PDF
through
the EasyChair conference submission system:
https://easychair.org/conferences/?conf=humanize2023
In order for accepted papers to be included in the proceedings, at
least
one author should be registered --
https://iui.acm.org/2023/index.html --
and attend the workshop.
# ORGANIZING COMMITTEE
Bruce Ferwerda --
bruce.ferwerda@ju.se
Department of Computer Science and Informatics
School of Engineering
Jönköping University, Sweden
http://www.bruceferwerda.com
Marko Tkalcic --
marko.tkalcic@unibz.it
Faculty of Computer Science
University of Primorska, Koper, Slovenia
http://markotkalcic.com/
Panagiotis Germanakos --
panagiotis.germanakos@sap.com
User Experience S/4HANA, Product Engineering
Intelligent Enterprise Group
SAP SE, Germany
PulseX Research Institute gUG, Germany
http://www.pgermanakos.com
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