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 …

Recommender systems are widely used in today’s society, but many of them do not meet users’ needs and therefore fail to reach their full potential. Without careful consideration, such systems can interfere with the natural decision-making process, resulting in the disregard for recommendations provided. …

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 …

Shima Mousavi Gargari - Professor by Special Appointment at the Research Center of Balanced Energy Systems at HAN University of Applied Sciences was speaker during the European Sustainable Energy Week 11-13 June 2024. The European Sustainable Energy Week brings together public authorities, private companies, …

Future work processes are going to change in several aspects. The working population (at least in Western European countries) is decreasing, while average age of employees increases. Their productivity is key to continuity in sectors like healthcare and manufacturing. Health and safety monitoring, combined …

Artificial Intelligence (AI) is increasingly shaping the way we work, live, and interact, leading to significant developments across various sectors of industry, including media, finance, business services, retail and education. In recent years, numerous high-level principles and guidelines for ‘responsible’ …

Throughout society, dialogues about Artificial Intelligence (AI) are dominated by a range of technophobic or techno-solutionist narratives. Both these narratives share a technological focus. To develop responsible AI, however, it is important to look at the systemic ecological and societal underpinnings …

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 …

With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured …

Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation …