Explainability of AI systems is branded as a solution to the black box problem. The EU AI Act introduces AI explainability as a requirement for high-risk AI systems, including a right to explanation. However, understanding the wording of the requirements and the related provider and deployer obligations …

Abstract from EHRCON25-openEHR International Conference 2025. The increasing demand for structured high-quality clinical data for AI and decision support systems highlights the shortcomings of current healthcare data infrastructure, particularly its misalignment with FAIR [1] data principles (Findable, …

Aletta Smits, professor in Human Centred Technology, zoomed in on human-AI interaction: how to give people autonomy and control. She introduced us to terms like recommender systems: any tool that helps you select one or more from a host of options; and decision support systems: any tool that helps you …

Efficient hydrogen (H2) storage remains a major challenge for clean energy applications. This study presents an AI-driven methodology to optimize H2 storage in porous carbon adsorbents. A comprehensive dataset of 917 literature-derived entries was used to develop two machine learning models: Random Forest …

How designers can contribute to responsible AI in the clinic

Methane, the primary component of natural gas, emits less carbon dioxide than other petroleum-based fuels but faces challenges in efficient storage and transportation. Advanced adsorption materials provide a safe and cost-effective solution, with metal–organic frameworks (MOFs) emerging as promising …

Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (ML), specifically deep learning, is increasingly integrated in industrial CPS, but the inherent complexity of ML models results …

The use and adoption of Artificial Intelligence (AI) in our society is growing rapidly (Maslej 2025). Alongside this growth, the importance of specific ‘competencies’ that users need to effectively interact with and critically evaluate AI is becoming increasingly important (Long en Magerko 2020). This …

Human-centered AI must confront tensions between mutually incompatible fairness definitions and fairness requirements of algorithmic decision-making (ADM) systems. To investigate how people perceive this trade-off and how this perception can guide engineering requirements, we determine the underlying …

This presentation explores AI literacy as a critical and situated dimension of Artistic Research (AR), drawing from recent investigation conducted with Master’s students in a discursive artistic AI course at Frank Mohr Institute. Using a structured questionnaire as both a pedagogical and research tool, …