In flexible education, recommender systems that support course selection, are considered a viable means to help students in making informed course selections, especially where curricula offer greater flexibility. However, these recommender systems present both potential benefits and looming risks, such as overdependence on technology, biased recommendations, and privacy issues. User control mechanisms in recommender interfaces (or algorithmic affordances) might offer options to address those risks, but they have not been systematically studied yet. This paper presents the outcomes of a design session conducted during the INTERACT23 workshop on Algorithmic Affordances in Recommender Interfaces. This design session yielded insights in how the design of an interface, and specifically the algorithmic affordances in these interfaces, may address the ethical risks and dilemmas of using a recommender in such an impactful context by potentially vulnerable users. Through design and reflection, we discovered a host of design ideas for the interface of a flexible education interface, that can serve as conversation starters for practitioners implementing flexible education. More research is needed to explore these design directions and to gain insights on how they can help to approximate more ethically operating recommender systems.
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This exploratory study investigates the rationale behind categorizing algorithmic controls, or algorithmic affordances, in the graphical user interfaces (GUIs) of recommender systems. Seven professionals from industry and academia took part in an open card sorting activity to analyze 45 cards with examples of algorithmic affordances in recommender systems’ GUIs. Their objective was to identify potential design patterns including features on which to base these patterns. Analyzing the group discussions revealed distinct thought processes and defining factors for design patterns that were shared by academic and industry partners. While the discussions were promising, they also demonstrated a varying degree of alignment between industry and academia when it came to labelling the identified categories. Since this workshop is part of the preparation for creating a design pattern library of algorithmic affordances, and since the library aims to be useful for both industry and research partners, further research into design patterns of algorithmic affordances, particularly in terms of labelling and description, is required in order to establish categories that resonate with all relevant parties
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Algorithmic affordances are defined as user interaction mechanisms that allow users tangible control over AI algorithms, such as recommender systems. Designing such algorithmic affordances, including assessing their impact, is not straightforward and practitioners state that they lack resources to design adequately for interfaces of AI systems. This could be amended by creating a comprehensive pattern library of algorithmic affordances. This library should provide easy access to patterns, supported by live examples and research on their experiential impact and limitations of use. The Algorithmic Affordances in Recommender Interfaces workshop aimed to address key challenges related to building such a pattern library, including pattern identification features, a framework for systematic impact evaluation, and understanding the interaction between algorithmic affordances and their context of use, especially in education or with users with a low algorithmic literacy. Preliminary solutions were proposed for these challenges.
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