Hardly Any Sliders in Real-World Apps: A Library of Algorithmic Affordances to Bridge the Research-Practice Gap in Human-AI Interaction

Abstract

Algorithmic affordances—interactive mechanisms that allow users to exercise tangible control over algorithms—play a crucial role in recommender systems. They can facilitate users’ sense of autonomy, transparency, and ultimately ownership over a recommender’s results, all qualities that are central to responsible AI. Designers, among others, are tasked with creating these interactions, yet state that they lack resources to do so effectively. At the same time, academic research into these interactions rarely crosses the research-practice gap. As a solution, designers call for a structured library of algorithmic affordances containing well-tested, well-founded, and up-to-date examples sourced from both real-world and experimental interfaces. Such a library should function as a boundary object, bridging academia and professional design practice. Academics could use it as a supplementary platform to disseminate their findings, while both practitioners and educators could draw upon it for inspiration and as a foundation for innovation. However, developing a library that accommodates multiple stakeholders presents several challenges, including the need to establish a common language for categorizing algorithmic affordances and devising a categorization of algorithmic affordances that is meaningful to all target groups. This research attempts to bring the designer perspective into this categorization.