What is it about?

Artificial Intelligence (AI) supports many of our everyday activities and decisions. However, personalized algorithmic recommendations often produce adverse experiences due to a lack of awareness, control, or transparency. While research has directed solutions on graphical user interfaces~(GUIs), there are no explorations of Tangible User Interfaces (TUIs) to improve the experience with such systems, despite the valid existing academic arguments in favor of this exploration. This paper centers on transparency and control to analyze how 18 movie recommender system users perceived four TUIs using individual co-design sessions and post-interview questionnaires.

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Why is it important?

The paper identifies seven design considerations while designing TUIs to interact with algorithmic movie recommender systems: (1) Distinctions between TUIs and GUIs; (2) TUIs replacing predominant interfaces; (3) Preference for single-device TUIs; (4) The relevance of granular control for TUIs; (5) Apparent transparency limitations of TUIs; (6) TUIs and algorithmic social computing; and (7) Overview of specific design choices, including advantages and disadvantages of soft, hard, rounded, cubic, and humanoid interfaces. These findings inspired Recffy: our proposal of the first functional TUI designed to enhance awareness and control in personalized movie recommendations.

Perspectives

Based on this study, we propose Tangible Algorithms: TUIs dedicated to improving the interaction of algorithmic systems and their profiling processes or decisions in a specific context. Furthermore, we describe the relevance of tangible algorithms and design guidelines to promote them in diverse AI contexts. The paper also invites to research tangible algorithms in the context of group recommendations

Oscar Alvarado
Universidad de Costa Rica

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This page is a summary of: Towards Tangible Algorithms: Exploring the Experiences of Tangible Interactions with Movie Recommender Algorithms, Proceedings of the ACM on Human-Computer Interaction, November 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3555757.
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