ECUMAP
Title of the Tutorial:
Ethical Considerations in User Modeling and Personalization (ECUMAP)
List of presenters:
- Jim Torresen, University of Oslo, Norway
- Atsushi Nakazawa, Kyoto University, Japan
Abstract:
Ethical considerations are getting increased attention with regards to providing responsible personalization for robots and autonomous systems. This is partly as a result of the currently limited deployment of such systems in human support and interaction settings. There are many different ethical considerations, and it is important to identify those relevant to one’s own work within user modeling and personalization. The tutorial will give an overview of the most commonly expressed ethical challenges and ways being undertaken to reduce their impact using the findings in an earlier undertaken review supplemented with recent work and initiatives. The includes the identified challenges in a “Statement on research ethics in artificial intelligence”.
The tutorial will exemplify the challenges related to privacy, security and safety through several examples from own and others’ work. With tutorial presenters from two different continents (Asia and Europe), the tutorial will cover the views on ethical assessment across countries and cultures. Thus, it will illustrate to what extent perspectives are similar and what are regarded as the most important ethical aspects in the two countries.
The tutorial will also contain interactive parts where the participants express and discuss their own views on ethical considerations through polls and by providing comments and questions as a part of the tutorial session.
Website:
https://jimtoer.no/tutorials-workshops/UMAP-2022-Tutorial-JimTorresen-web.html
SCoRe
Title of the Tutorial:
Semantics-aware Content Representations for Reproducible Recommender Systems (SCoRe)
List of presenters:
- Pasquale Lops, University of Bari Aldo Moro, Italy
- Cataldo Musto, University of Bari Aldo Moro, Italy
- Marco Polignano, University of Bari Aldo Moro, Italy
Abstract:
Content-based recommender systems suggest items similar to those the user already liked in the past by building a representation of users and items based on descriptive features, usually obtained by processing textual content. A sharp limitation of classic keyword-based
representations are not often enough to correctly catch the user preferences, as well as the informative content conveyed by the items. Of course, a sub-optimal comprehension of the informative content leads to a sub-optimal representation of users and items and, in turn, to recommendations which are not accurate. Hence, it is necessary to improve such representations in order to fully exploit the potential of content-based features and textual data. Semantics-aware recommender systems represent one of the most innovative lines of research in which the goal is to use semantic approaches for representing content. Thanks to these representations, it is possible to give meaning to information expressed in natural language and to obtain its deeper comprehension. The literature on semantics-aware recommender systems is actually rich, constantly evolving, but unfortunately also scattered, even in terms of software libraries to implement them. This tutorial aims to present, from both a theoretical and practical point of view, semantics-aware techniques for content representation, with the purpose of realizing effective and accountable recommender systems. Indeed, the experimental workflow related to recommender systems is becoming more and more complex, making the reproducibility of experiments a challenge. Hence, this tutorial provides, on the one hand, a common ground for both researchers and practitioners interested in the latest semantics-aware techniques for user modeling and recommender systems, while on the other hand, it will introduce a new and comprehensive Python framework called ClayRS, developed by our research group, which will make the entire recommendation pipeline simple, fast, and replicable.
Website:
http://www.di.uniba.it/~swap/score/
UCAIA
Title of the Tutorial:
User Control in Adaptive Information Access
List of presenters:
- Peter Brusilovsky, University of Pittsburgh, USA
Abstract:
In recent years, the use of Artificial Intelligence (AI) technologies has expanded to many areas where they directly affect the lives of many people. AI-based approaches advise human decision-makers who should be released on bail, whether it is a good time to discharge a patient from a hospital and whether a specific student is at risk to fail a course. Such an extensive use in AI in decision making came with a range of potential problems that have been extensively studied over the last few years. Recognition of these problems motivated a rapid rise of research on “human-centered AI”, which attempted to address and minimize the negative effects of using AI technologies. Among the ideas of human-centered AI is user control – engaging users in affecting AI decision making to prevent possible errors and biases. In my talk, I will focus on the application of user control in one popular area of AI application, adaptive information access. Adaptive information access systems such as personalized search and recommender systems attempt to model their users to help them in finding the most relevant information. Yet, user modeling and personalization mechanisms might not always work as expected resulting in errors, biases, and suboptimal behavior. Combining the decision power or AI with the ability of the user to guide and control it brings together the strong sides of artificial and human intelligence and could lead to better results. This tutorial will provide a systematic review of approaches focused on adding various kinds of user control to adaptive information access systems and discuss lessons learned, prospects, and challenges of this direction of research.
Website:
TBA.