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' …

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 …

Experimental Learning and Innovation Environments, such as Living Labs, Field Labs, and Urban Innovation Labs, are increasingly used to connect multi-stakeholders in envisioning, creating, experimenting, learning, and trying out novel responses to diverse societal challenges. With designers facilitating …

As AI systems become increasingly prevalent in our daily lives and work, it is essential to contemplate their social role and how they interact with us. While functionality and increasingly explainability and trustworthiness are often the primary focus in designing AI systems, little consideration is …

Digitalization enables public organizations to personalize their services, tuning them to the specific situation, abilities, and preferences of the citizens. At the same time, digital services can be experienced as being less personal than face-to-face contact by citizens. The large existing volume of …