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ACM IUI Workshop -- HUMANIZE 2021 CALL FOR PAPERS
The 5th International Workshop on Transparency and Explainability
in Adaptive Systems through User Modeling Grounded in
Psychological Theory (HUMANIZE), in conjunction with the 26th ACM
Conference on Intelligent User Interfaces (ACM IUI 2021), Texas,
USA, 13-17 April 2021
Full details are available online:
http://www.humanize-workshop.org/
# IMPORTANT DATES
- Submission Deadline: 23 December 2020
- Notification to Authors: 31 January 2021
- Camera-ready: TBD
# 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 SigConf format. Use either
the Microsoft Word template or the LaTeX template:
- Microsoft Word:
http://st.sigchi.org/sigchi-paper-template/SIGCHIPaperFormat.docx
- LaTex:
https://github.com/sigchi/Document-Formats/tree/master/LaTeX
# 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=humanize2021
In order for accepted papers to be included in the proceedings, at
least one author should be registered --
https://iui.acm.org/2021/index.html -- and attend the workshop.
# ORGANIZING COMMITTEE
Mark Graus --
mp.graus@maastrichtuniversity.nl
Department of Marketing and Supply Chain Management
School of Business and Economics
Maastricht University, the Netherlands
http://www.markgraus.net
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
Department of Computer Science
University of Cyprus, Cyprus
http://scrat.cs.ucy.ac.cy/pgerman/
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