This guide was developed for designers and developers of AI systems, with the goal of ensuring that these systems are sufficiently explainable. Sufficient here means that it meets the legal re quirements from AI Act and GDPR and that users can use the system properly. Explainability of decisions is …

Deze handreiking is ontwikkeld voor designers en ontwikkelaars van AI-systemen, met als doel om te zorgen dat deze systemen voldoende uitlegbaar zijn. Voldoende betekent hier dat het vol doet aan de wettelijke eisen vanuit AI Act en AVG en dat gebruikers het systeem goed kunnen gebruiken. In deze handreiking …

Today’s applied design researchers are where the action is. They have always operated as a catalyst of change: propelling and steering design and society into brighter futures. But now, more than ever, design researchers teamup with a broad range of stakeholders to get things done. They surround themselves …

In a rapidly evolving world, the need for innovative approaches to societal challenges is more critical than ever. As partners of the Network Applied Design Research (NADR), we believe that applied design research can be a promising approach for addressing complex issues in many domains, such as health-care, …

In this paper, we explore the design of web-based advice robots to enhance users' confidence in acting upon the provided advice. Drawing from research on algorithm acceptance and explainable AI, we hypothesise four design principles that may encourage interactivity and exploration, thus fostering users' …

Design academics struggle in effectively reaching out to design practice, while design practitioners have difficulties in appropriating academic output. In their turn, design practitioners create new local knowledge that may not be recognised (as such) by design academics. This situation is seen as suboptimal …

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 …

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 …

The user’s experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as controllability, trust, and autonomy, to gauge their impact. As part of our effort to systematically categorize …

Design schools in digital media and interaction design face the challenge of integrating recent artificial intelligence (AI) advancements into their curriculum. To address this, curricula must teach students to design both "with" and "for" AI. This paper addresses how designing for AI differs from designing …