Abstract

– Claudia Wagner

Foundations of Computational Social Science: From Origins to Infrastructure

How did computational social science emerge, and what defines the field today? This lecture takes a journey from the early days of digital data to the current landscape shaped by large-scale data collection, AI methods, and interdisciplinary collaboration. We introduce the core pillars of CSS—data, computational methods, and theory—and discuss how they jointly enable new ways of understanding social phenomena. Finally, we consider how research infrastructure—such as data collections, tools, and collaborative environments—supports CSS research, and what challenges and opportunities lie ahead.

– Luca Pappalardo

Human-AI feedback loops and their impact on society

Recommender systems and AI assistants have become ubiquitous, fundamentally reshaping human decision-making across digital platforms. This creates a continuous, self-reinforcing feedback loop: user choices generate the training data for AI models, which then refine their outputs to further influence future user preferences. This dynamic differs significantly from traditional human-machine interaction, often yielding complex and unintended systemic consequences. We will discuss the concept of human-AI co-evolution as a framework for understanding these interactions. We will evaluate the strengths and limitations of current methodologies, highlighting critical shortcomings in capturing feedback mechanisms. By examining diverse real-world ecosystems (social media, urban, online retail, and generative AI platforms) we will identify the multidisciplinary challenges inherent in establishing this field of study. Finally, we conceptualize these hurdles across three increasing levels of abstraction (scientific and legal) to provide a roadmap for future research into the long-term impacts of AI-driven influence.