The rapid advancement of artificial intelligence (AI) presents profound regulatory challenges, as emerging technologies often outpace traditional legislative frameworks. This article critically examines the European Union’s AI Act as a case study in regulating AI, highlighting the inherent tension between the law’s stability and the need for flexibility in governing innovation. The AI Act employs a risk-based approach, categorising AI systems according to their potential societal risks and introducing a multifaceted regulatory governance structure incorporating statutory, administrative, and outsourced legislative policy models. The study identifies key challenges arising from this fragmented regulatory approach, including legal uncertainty, inconsistencies, and concerns over political accountability. Through a comparative analysis of four legislative policy models—statutory, administrative, judicial, and outsourced—the article argues for an administrative model centred on a dedicated EU AI Agency. This proposed model aims to balance regulatory adaptability with legal certainty by consolidating expertise, ensuring procedural clarity, and strengthening accountability. By outlining a refined governance structure, this research offers a preliminary blueprint for a more coherent regulatory framework for emerging technologies, particularly AI.

This one-day workshop explores how encounters with regulatory aspects, before, during and after design, can shape the production of more sustainable futures. Within CSCW and Sustainable HCI, there have been repeated calls to develop more systemic approaches to design that include the political, institutional, and infrastructural aspects underpinning technology-mediated interventions towards sustainability. This workshop targets these calls by drawing attention to the methodological, analytical, and design challenges of considering regulatory and policy-making aspects that can shape both the design and the processes of designing digital technologies. It will be an opportunity to bring together CSCW, Sustainability, Law and other interested scholars, representatives of public institutions, environmental collectives, and diverse actors interested in investigating the "knots" of relations between technology designs, regulatory aspects, and sustainable practices. We plan to accept up to twenty contributions and run the workshop on-site.

When designing with and for complex sustainability processes like waste management, it is crucial to understand digital technologies as entangled with broader systemic factors, including physical infrastructures and regulatory instruments. Within the case of organic household waste management, this pictorial aims at making such relations visible through design methods. We have used yarn to represent the different threads of these entanglements and defined specific configurations: tangles, knots, loose ends, and frayed threads. We discuss how the design practice of giving form to these entanglements can make complex relations between digital technology, infrastructures, and regulatory instruments more visible and actionable for HCI, and explore how digital technologies are – and can be – made to work within them.

We demonstrate the construction of robust counterfactual explanations for support vector machines (SVM), where the privacy mechanism that publicly releases the classifier guarantees differential privacy. Privacy preservation is essential when dealing with sensitive data, such as in applications within the health domain. In addition, providing explanations for machine learning predictions is an important requirement within so-called high risk applications, as referred to in the EU AI Act. Thus, the innovative aspects of this work correspond to studying the interaction between three desired aspects: accuracy, privacy, and explainability. The SVM classification accuracy is affected by the privacy mechanism through the introduced perturbations in the classifier weights. Consequently, we need to consider a trade-off between accuracy and privacy. In addition, counterfactual explanations, which quantify the smallest changes to selected data instances in order to change their classification, may become not credible when we have data privacy guarantees. Hence, robustness for counterfactual explanations is needed in order to create confidence about the credibility of the explanations. Our demonstrator provides an interactive environment to show the interplay between the considered aspects of accuracy, privacy, and explainability.