Workshop Details
ExUM workshop aims to provide a forum to discuss and investigate the role of transparency and explainability in the development of novel methodologies to build user models and personalized systems. Research lines of interest for ExUM include: building scrutable user models and transparent algorithms, analyzing the impact of opaque algorithms on final users, studying the role of explanation strategies, investigating how to provide users with more control in personalized and adaptive systems.
Adaptive and personalized systems have become pervasive technologies which are gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interact every day with algorithms that help us in several scenarios, ranging from services that suggest us music to be listened to or movies to be watched, to personal assistants able to proactively support us in complex decision-making tasks.
As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the recent General Data Protection Regulation (GDPR) emphasized the users' right to explanation when people face machine learningbased (or – more in general artificial intelligence-based) systems. Unfortunately, the current research tends to go in the opposite direction, since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of the explainability and the transparency of the model.
The main research questions which arise from this scenario is simple and straightforward: how can we deal with such a dichotomy between the need for effective adaptive systems and the right to transparency and interpretability?
Several research lines are triggered by this question: building scrutable user models and transparent algorithms, analyzing the impact of opaque algorithms on final users, studying the role of explanation strategies, investigating how to provide users with more control in the personalization and adaptation problems.
Topics of interests include but are not limited
to:
Transparent and Explainable Personalization Strategies
o
Scrutable User Models
o
Transparent User Profiling and
Personal Data Extraction
o
Explainable Personalization
and Adaptation Methodologies
o
Novel strategies (e.g.,
conversational recommender systems) for building transparent algorithms
Designing Explanation Algorithms
o
Explanation algorithms based
on item description and item properties
o
Explanation algorithms based
on user-generated content (e.g., reviews)
o
Explanation algorithms based
on collaborative information
o
Building explanation
algorithms for opaque personalization techniques (e.g., neural networks, matrix
factorization)
Designing Transparent and Explainable User Interfaces
o
Transparent User Interfaces
o
Designing Transparent
Interaction methodologies
o
Novel paradigms (e.g. chatbots)
for building transparent models
Evaluating Transparency and Explainability
o
Evaluating Transparency in
interaction or personalization
o
Evaluating Explainability of
the algorithms
o
Designing User Studies for
evaluating transparency and explainability
o
Novel metrics and
experimental protocols
Open Issues in Transparent and Explainable User Models and Personalized
Systems
o
Ethical issues (Fairness and
Biases) in User Models and Personalized Systems
o
Privacy management of
Personal and Social data
o
Discussing Recent
Regulations (GDPR) and future directions
