13 Dec
2021
13 Dec
'21
10:02 a.m.
— Apologies for cross-posting —
Special Issue "Explainable User Models"
A special issue of Multimodal Technologies and
Interaction<https://www.mdpi.com/journal/mti> (ISSN 2414-4088).
Important Dates & Facts:
Manuscripts due by: February 20, 2022
Notification to authors: March 15, 2022
Website: https://www.mdpi.com/journal/mti/special_issues/Explainable_User_Models
Benefits of submission:
- Experienced Guest Editor
- Open Access with quick processing time
- High Visibility: Indexed within
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- Journal Rank: CiteScore - Q2 (Computer Science Applications)
Special Issue Information
This special issue addresses research on Explainable User Models. As AI systems’ actions
and decisions will significantly affect their users, it is important to be able to
understand how an AI system represents its users. It is a well-known hurdle that many AI
algorithms behave largely as black boxes. One key aim of explainability is, therefore, to
make the inner workings of AI systems more accessible and transparent.
Such explanations can be helpful in the case when the system uses information about the
user to develop a working representation of the user, and then uses this representation to
adjust or inform system behavior. E.g., an educational system could detect whether
students have a more internal or external locus of control, a music recommender system
could adapt the music it is playing to the current mood of a user, or an aviation system
could detect the visual memory capacity of its pilots. However, when adapting to such
user models it is crucial that these models are accurately detected. Furthermore, for such
explanations to be useful, they need to be able to explain or justify their
representations of users in a human-understandable way. This creates a necessity for
techniques that will create models for the automatic generation of satisfactory
explanations intelligible for human users interacting with the system.
The scope of the special issue includes but is not limited to:
Detection and Modelling
• Novel ways of Modeling User Preferences
• Types of information to model (Knowledge, Personality, Cognitive differences, etc.)
• Distinguishing between stationary versus transient user models (e.g., Personality vs
Mood)
• Context modeling (e.g., at work versus at home, lean in versus lean out activities)
• User models from heterogeneous sources (e.g., behavior, ratings, and reviews)
• Enrichment and Crowdsourcing for Explainable User Models
Ethics
• Detection of sensitive or rarely reported attributes (e.g., gender, race, sexial
orientation)
• Implicit user modeling versus explicit user modeling (e.g., questionnaires versus
inference from behavior)
• User modeling for self actualization (e.g., user modeling to improve dietary or news
consumption habits)
Human understandability
• Metrics and methodologies for evaluating fitness for the purpose of explanations
• Balancing completeness and understandability for complex user models
• Explanations to mitigate human biases (e.g., confirmation bias, anchoring)
• Effect of user model explanation on subsequent user interaction (e.g., simulations, and
novel evaluation methodologies)
Effectiveness
• Analysis or comparison of context of use of explanation (e.g., risk, time pressure,
error tolerance)
• Analysis of context of use of system (e.g., decision support, prediction)
• Analysis or comparison of effect of explaining in specific domains (e.g., education,
health, recruitment, security)
Adaptive presentation of the explanations
• For different types of user
• Interactive explanations
• Investigation of which presentational aspects are beneficial to tailor in the
explanation (e.g., level of detail, terminology, modality text or graphics, level of
interaction)
Prof. Dr. Nava Tintarev
Ms. Oana Inel
Guest Editors