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Respect for Human Autonomy in Recommender Systems
Recommender systems can influence human behavior in significant ways, in...
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Human Aspects and Perception of Privacy in Relation to Personalization
The concept of privacy is inherently intertwined with human attitudes an...
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Ethics of Food Recommender Applications
The recent unprecedented popularity of food recommender applications has...
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Random Graphs for Performance Evaluation of Recommender Systems
The purpose of this article is to introduce a new analytical framework d...
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How Personal is Machine Learning Personalization?
Though used extensively, the concept and process of machine learning (ML...
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Session-Based Recommender Systems for Action Selection in GUI Test Generation
Test generation at the graphical user interface (GUI) level has proven t...
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Fostering Bilateral Patient-Clinician Engagement in Active Self-Tracking of Subjective Experience
In this position paper we describe select aspects of our experience with...
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Beyond Our Behavior: The GDPR and Humanistic Personalization
Personalization should take the human person seriously. This requires a deeper understanding of how recommender systems can shape both our self-understanding and identity. We unpack key European humanistic and philosophical ideas underlying the General Data Protection Regulation (GDPR) and propose a new paradigm of humanistic personalization. Humanistic personalization responds to the IEEE's call for Ethically Aligned Design (EAD) and is based on fundamental human capacities and values. Humanistic personalization focuses on narrative accuracy: the subjective fit between a person's self-narrative and both the input (personal data) and output of a recommender system. In doing so, we re-frame the distinction between implicit and explicit data collection as one of nonconscious ("organismic") behavior and conscious ("reflective") action. This distinction raises important ethical and interpretive issues related to agency, self-understanding, and political participation. Finally, we discuss how an emphasis on narrative accuracy can reduce opportunities for epistemic injustice done to data subjects.
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