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HUMANIZE 2020 : 4th Workshop on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory


When Mar 17, 2020 - Mar 17, 2020
Where Cagliari (Italy)
Submission Deadline Dec 20, 2019
Notification Due Jan 14, 2020
Categories    artificial intelligence

Call For Papers

*Please forward to anyone who might be interested*
Apologies for cross-posting.

First Call for Papers:
4th Workshop on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory (HUMANIZE)

March 17, 2020, Cagliari (Italy)

In conjunction with IUI 2020


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:

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.

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.

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.

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.


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.


For this workshop we encourage three kinds of submissions:

- Full papers (anonymized 8-10 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:
- LaTex:

- December 20, 2019: Submission Deadline
- January 14, 2020: Notification to Authors
- March 17, 2020: Workshop at IUI 2020 (Cagliari, Italy)


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:

Accepted submissions will be included in the joint ACM IUI workshop proceedings published as a CEUR-WS volume. In order for accepted papers to be included, at least one author should be registered ( and attend the workshop.


Mark Graus �
Department of Marketing and Supply Chain Management
School of Business and Economics
Maastricht University, the Netherlands

Bruce Ferwerda �
Department of Computer Science and Informatics
School of Engineering
J�nk�ping University, Sweden

Marko Tkalcic �
Faculty of Computer Science
University of Primorska, Koper, Slovenia

Panagiotis Germanakos �
UX, Mobile & Business Services
P&I Industry Cloud & Custom Development
SAP SE, Germany

Department of Computer Science
University of Cyprus, Cyprus

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