[AISWorld] ACM IUI Workshop -- HUMANIZE 2021 *EXTENDED* CALL FOR PAPERS

[pgerman]

ACM IUI Workshop -- HUMANIZE 2021 *EXTENDED* 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: 15 January 2021 (extended)
- 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 at 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 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

Department of Computer Science
University of Cyprus, Cyprus
http://scrat.cs.ucy.ac.cy/pgerman/