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 user experience of our daily interactions is increasingly shaped with the aid of AI, mostly as the output of recommendation engines. However, it is less common to present users with possibilities to navigate or adapt such output. In this paper we argue that adding such algorithmic controls can be a potent strategy to create explainable AI and to aid users in building adequate mental models of the system. We describe our efforts to create a pattern library for algorithmic controls: the algorithmic affordances pattern library. The library can aid in bridging research efforts to explore and evaluate algorithmic controls and emerging practices in commercial applications, therewith scaffolding a more evidence-based adoption of algorithmic controls in industry. A first version of the library suggested four distinct categories of algorithmic controls: feeding the algorithm, tuning algorithmic parameters, activating recommendation contexts, and navigating the recommendation space. In this paper we discuss these and reflect on how each of them could aid explainability. Based on this reflection, we unfold a sketch for a future research agenda. The paper also serves as an open invitation to the XAI community to strengthen our approach with things we missed so far.
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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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Moderatie van lezersreacties onder nieuwsartikelen is erg arbeidsintensief. Met behulp van kunstmatige intelligentie wordt moderatie mogelijk tegen een redelijke prijs. Aangezien elke toepassing van kunstmatige intelligentie eerlijk en transparant moet zijn, is het belangrijk om te onderzoeken hoe media hieraan kunnen voldoen.
Moderatie van lezersreacties onder nieuwsartikelen is erg arbeidsintensief. Met behulp van kunstmatige intelligentie wordt moderatie mogelijk tegen een redelijke prijs. Aangezien elke toepassing van kunstmatige intelligentie eerlijk en transparant moet zijn, is het belangrijk om te onderzoeken hoe media hieraan kunnen voldoen.Doel Dit promotieproject zal zich richten op de rechtvaardigheid, accountability en transparantie van algoritmische systemen voor het modereren van lezersreacties. Het biedt een theoretisch kader en bruikbare matregelen die nieuwsorganisaties zullen ondersteunen in het naleven van recente beleidsvorming voor een waardegedreven implementatie van AI. Nu steeds meer nieuwsmedia AI gaan gebruiken, moeten ze rechtvaardigheid, accountability en transparantie in hun gebruik van algoritmen meenemen in hun werkwijzen. Resultaten Hoewel moderatie met AI zeer aantrekkelijk is vanuit economisch oogpunt, moeten nieuwsmedia weten hoe ze onnauwkeurigheid en bias kunnen verminderen (fairness), de werking van hun AI bekendmaken (accountability) en de gebruikers laten begrijpen hoe beslissingen via AI worden genomen (transparancy). Dit proefschrift bevordert de kennis over deze onderwerpen. Looptijd 01 februari 2022 - 01 februari 2025 Aanpak De centrale onderzoeksvraag van dit promotieonderzoek is: Hoe kunnen en moeten nieuwsmedia rechtvaardigheid, accountability en transparantie in hun gebruik van algoritmes voor commentmoderatie? Om deze vraag te beantwoorden is het onderzoek opgesplitst in vier deelvragen. Hoe gebruiken nieuwsmedia algoritmes voor het modereren van reacties? Wat kunnen nieuwsmedia doen om onnauwkeurigheid en bias bij het modereren via AI van reacties te verminderen? Wat moeten nieuwsmedia bekendmaken over hun gebruik van moderatie via AI? Wat maakt uitleg van moderatie via AI begrijpelijk voor gebruikers van verschillende niveaus van digitale competentie?
