TL;DR
This thesis argues that issues of AI safety, alignment, explainability, and transparency require sustained sociotechnical inquiry rather than pure technical fixes.
A five-dimensional diagnostic framework that maps the challenges of human-AI collaboration across Integration, Representation, Scale, Temporality, and Adequacy gaps and shows that augmentation remains the dominant and most viable mode of use in complex environments.
This article develops a philosophical-conceptual framework for rethinking Explainable Artificial Intelligence (XAI) and Human-Centred Artificial Intelligence (HCAI) through the pragmatic semiotics of Charles Sanders Peirce. Rather than treating explainability solely as algorithmic transparency, the article conceptualiz...
Research on Artificial Intelligence (AI) and the circular economy (CE) has largely examined how AI can improve circular operations. Much less attention has been paid to AI tools widely used to understand and reason about the CE, particularly large language models (LLMs) such as ChatGPT. Researchers, practitioners, st...
The regulatory sandboxes can be viewed as pedagogical environments for AI: dynamic spaces where alignment develops as a formative process, progressively shaping autonomous behaviors through interaction and cooperation in scenarios of increasing complexity.
Using the phenomenology of agent-based modeling (ABM), it is argued that the incorporation of agentic AI methods that facilitate the automation and minimal human intervention in the application of social behavior represents the next challenge and stage of AI in understanding the past.
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