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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Introduction: Sensor-feedback systems can be used to support people after stroke during independent practice of gait. The main aim of the study was to describe the user-centred approach to (re)design the user interface of the sensor feedback system “Stappy” for people after stroke, and share the deliverables and key observations from this process. Methods: The user-centred approach was structured around four phases (the discovery, definition, development and delivery phase) which were fundamental to the design process. Fifteen participants with cognitive and/or physical limitations participated (10 women, 2/3 older than 65). Prototypes were evaluated in multiple test rounds, consisting of 2–7 individual test sessions. Results: Seven deliverables were created: a list of design requirements, a personae, a user flow, a low-, medium- and high-fidelity prototype and the character “Stappy”. The first six deliverables were necessary tools to design the user interface, whereas the character was a solution resulting from this design process. Key observations related to “readability and contrast of visual information”, “understanding and remembering information”, “physical limitations” were confirmed by and “empathy” was additionally derived from the design process. Conclusions: The study offers a structured methodology resulting in deliverables and key observations, which can be used to (re)design meaningful user interfaces for people after stroke. Additionally, the study provides a technique that may promote “empathy” through the creation of the character Stappy. The description may provide guidance for health care professionals, researchers or designers in future user interface design projects in which existing products are redesigned for people after stroke.
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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