Advances in artificial intelligence, sensors and big data management have far-reaching societal impacts. As these systems augment our everyday lives, it becomes increasing-ly important for people to understand them and remain in control. We investigate how HCI researchers can help to develop accountable systems by performing a literature analysis of 289 core papers on explanations and explaina-ble systems, as well as 12,412 citing papers. Using topic modeling, co-occurrence and network analysis, we mapped the research space from diverse domains, such as algorith-mic accountability, interpretable machine learning, context-awareness, cognitive psychology, and software learnability. We reveal fading and burgeoning trends in explainable systems, and identify domains that are closely connected or mostly isolated. The time is ripe for the HCI community to ensure that the powerful new autonomous systems have intelligible interfaces built-in. From our results, we propose several implications and directions for future research to-wards this goal.

https://dl.acm.org/doi/pdf/10.1145/3173574.3174156

Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda

Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The work discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and cross-representation. Finally, the work reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users.

/pdf/mediation-of-user-models-for-enhanced-personalization-in-yrwn3xo8b0.pdf

Mediation of User Models: for Enhanced Personalization in Recommender Systems

Personalization technologies offer powerful tools for enhancing the user experience in a wide variety of systems, but at the same time raise new privacy concerns. For example, systems that personalize advertisements according to the physical location of the user or according to the user's friends' search history, introduce new privacy risks that may discourage wide adoption of personalization technologies. This article analyzes the privacy risks associated with several current and prominent personalization trends, namely social-based personalization, behavioral profiling, and location-based personalization. We survey user attitudes towards privacy and personalization, as well as technologies that can help reduce privacy risks. We conclude with a discussion that frames risks and technical solutions in the intersection between personalization and privacy, as well as areas for further investigation. This frameworks can help designers and researchers to contextualize privacy challenges of solutions when designing personalization systems.

https://link.springer.com/content/pdf/10.1007%2Fs11257-011-9110-z.pdf

Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

This paper deals in depth with some of the emotions that play a role in a group recommender system, which recommends sequences of items to a group of users. First, it describes algorithms to model and predict the satisfaction experienced by individuals. Satisfaction is treated as an affective state. In particular, we model the decay of emotion over time and assimilation effects, where the affective state produced by previous items influences the impact on satisfaction of the next item. We compare the algorithms with each other, and investigate the effect of parameter values by comparing the algorithms' predictions with the results of an earlier empirical study. We discuss the difficulty of evaluating affective models, and present an experiment in a learning domain to show how some empirical evaluation can be done. Secondly, this paper proposes modifications to the algorithms to deal with the effect on an individual's satisfaction of that of others in the group. In particular, we model emotional contagion and conformity, and consider the impact of different relationship types. Thirdly, this paper explores the issue of privacy (feeling safe, not accidentally disclosing private tastes to others in the group) which is related to the emotion of embarrassment. It investigates the effect on privacy of different group aggregation strategies and proposes to add a virtual member to the group to further improve privacy.

/pdf/in-pursuit-of-satisfaction-and-the-prevention-of-35mlv8l525.pdf

In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems

/pdf/ubiquitous-user-modeling-4328oxbmpd.pdf

Ubiquitous User Modeling

This paper explores the question: How should we modify and extend existing UM Languages and representations to address specific ubiquitous computing issues? The answer we propose builds upon previous work on single user model server and exploits the user model representation to enable users to permit parts of the environment to access selective personas, which allow the user to provide one persona to one application and a different persona to another. We discuss the way that sensors can contribute to the user model in an ubiquitous computing environment. We also describe the ways that we address the issues of reusability and group models in ubiquitous computing.

/pdf/managing-private-user-models-and-shared-personas-2ifzfgnfwf.pdf

Managing private user models and shared personas

This issue's Works in Progress department presents six abstracts for projects that are developing interesting solutions to the elderly's quality of life challenges. The first two abstracts discuss projects that will help provide the elderly with freedom and independence by instrumenting their environments with supportive technology. The next two abstracts discuss projects building specialized user interfaces for addressing some of the challenges associated with aging, such as vision impairment. The final two abstracts present projects that will aid independence for the elderly by providing remote monitoring and assistance.

R. Kummerfeld

Papers

Managing private user models and shared personas

Successful Aging

Towards life-long personalization across multiple devices: The case of personal career management