[AISWorld] ACM IUI Workshop -- HUMANIZE 2023 2nd CALL FOR PAPERS

[Panagiotis Germanakos]

ACM IUI Workshop -- HUMANIZE 2023 SECOND 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, 27-31 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 at ju.se
Department of Computer Science and Informatics
School of Engineering
Jönköping University, Sweden
http://www.bruceferwerda.com


Marko Tkalcic -- marko.tkalcic at unibz.it
Faculty of Computer Science
University of Primorska, Koper, Slovenia
http://markotkalcic.com/


Panagiotis Germanakos -- panagiotis.germanakos at sap.com
User Experience S/4HANA, Product Engineering
Intelligent Enterprise Group
SAP SE, Germany
PulseX Research Institute gUG, Germany
http://www.pgermanakos.com