The paper "Environmental Slow AI: Design Principles for Generative Systems" argues that generative AI reflects embedded cultural values that can be reshaped. It proposes five design principles—restraint, sufficiency, selectivity over retention, material visibility, and friction as affordance—grounded in environmental sustainability and the Slow AI tradition. Each principle is illustrated with current system designs and operates at both implementation and interpretive levels to restore human agency and encourage reflective engagement.
arXiv:2606. 09006v1 Announce Type: cross Abstract: Both digital economy and digital technology researchers increasingly recognize the need to better address the role that artificial intelligence (AI) plays in shaping the evolution of the environmental, social and governance aspects of development.
Generative AI tools for creative work tend to be designed around the goal of removing friction, on the assumption that smoother iteration and faster output translate into more value for the designer. We argue, however, that this framing leaves out something important about how design ideation works, namely reflection-in-action.
arXiv:2607. 07521v1 Announce Type: cross Abstract: AI agents that generate final answers based on user input often do not meet the needs of creative fields.
AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and architecture need interactive systems that help users externalise and develop ideas, explore alternatives, and refine partial solutions.
arXiv:2609.07715v1 Announce Type: cross Abstract: The hype around generative AI seems to promise unprecedented productivity (and learning) gains. However, these technologies' increasing agentic featu...
arXiv:2607. 26827v1 Announce Type: cross Abstract: Generative AI tools for creative work tend to be designed around the goal of removing friction, on the assumption that smoother iteration and faster output translate into more value for the designer.
arXiv:2502. 20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social.
The paper proposes a diagnostic analogy framework to analyze how generative AI reshapes cultural and epistemic practices. It breaks down interventions into three coordinates—epistemic site, governing logic, and technical mechanism—to differentiate structural consequences from those that can be designed. Applying this framework to information discovery and knowledge synthesis, the authors illustrate how shifts from indexicality to inference and from editorial authority to statistical consensus generate specific cultural effects and uncover governance levers hidden by broad analogies.
Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings revea...
arXiv:2606. 13704v1 Announce Type: cross Abstract: This position paper argues that contemporary AI paradigms are insufficient for supporting complex global goals and introduces Planet-Centered AI (PCAI) as a design philosophy and research agenda that reorients AI toward planetary-scale socio-ecological systems and their long-term trajectories.
arXiv:2602. 19718v2 Announce Type: replace-cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activities.
arXiv:2608.24748v1 Announce Type: cross Abstract: How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixe...