Recommenders play a significant role in our daily lives, making decisions for users on a regular basis. Their widespread adoption necessitates a thorough examination of how users interact with recommenders and the algorithms that drive them. An important form of interaction in these systems are algorithmic affordances: means that provide users with perceptible control over the algorithm by, for instance, providing context (‘find a movie for this profile’), weighing criteria (‘most important is the main actor’), or evaluating results (‘loved this movie’). The assumption is that these algorithmic affordances impact interaction qualities such as transparency, trust, autonomy, and serendipity, and as a result, they impact the user experience. Currently, the precise nature of the relation between algorithmic affordances, their specific implementations in the interface, interaction qualities, and user experience remains unclear. Subjects that will be discussed during the workshop, therefore, include but are not limited to the impact of algorithmic affordances and their implementations on interaction qualities, balances between cognitive overload and transparency in recommender interfaces containing algorithmic affordances; and reasons why research into these types of interfaces sometimes fails to cross the research-practice gap and are not landing in the design practice. As a potential solution the workshop committee proposes a library of examples of algorithmic affordances design patterns and their implementations in recommender interfaces enriched with academic research concerning their impact. The final part of the workshop will be dedicated to formulating guiding principles for such a library.
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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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The online lifeworld gives adolescents various opportunities to meet their developmental needs. Not all young people benefit from these opportunities. They encounter negative experiences, have difficulties fulfilling their needs and engage in risky and harmful behaviours in the online lifeworld. This poses challenges for Dutch youth work professionals, as little is known about the digital lives of Dutch adolescents and the challenges they encounter when meeting their developmental needs in the online lifeworld. In this qualitative study, a photovoice method was used to collect screenshots from adolescents (N = 175) concerning their experiences and needs in the online lifeworld. Six types of developmental needs in the online lifeworld were distinguished. The article concludes that understanding how adolescents use online affordances to fulfil their developmental needs is a starting point for all youth work professionals in providing adequate support to adolescents in the online lifeworld.
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