Generative AI · Feedback · Adaptation

Information dynamics in human–AI systems

How does information evolve when humans and AI systems participate in feedback loops and shape each other?

Overview

This research theme examines how information evolves when humans and AI systems communicate and interact with each other. I am interested in generative AI not only as a technical artefact, but as a latent field shaping cultural, organisational, and epistemic dynamics.

Cybernetics offers a practical language for studying these systems: communication, feedback, adaptation, observation, and recursive change.

Relevant publications

This list is intended for publications connected to human–AI interaction, generative AI, communication, feedback, adaptation, and information dynamics.

Semantic topologies in the recursive application of generative AI models

Authors: Ben Swift, Sungyeon Hong
Year: 2026
Conference: 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Generative AI systems increasingly communicate not only with humans but also with other AI systems. This study investigates what happens when information is repeatedly translated between text and images by different generative models. Using clustering and topological data analysis, we show how semantic meaning drifts, stabilises, or transforms through recursive AI-mediated communication, offering new ways to study information dynamics in complex AI ecosystems.

A scenario-based design pack for exploring multimodal human–GenAI relations

Authors: Josh Andres, Chris Danta, Andrea Bianchi, Sahar Farzanfar, Gloria Milena Fernandez-Nieto, Alexa Becker, Tara Capel, Frances Liddell, Shelby Hagemann, Ned Cooper, Sungyeon Hong, Li Lin, Eduardo Benitez Sandoval, Anna Brynskov, Hubert Dariusz Zając, Zhuying Li, Tianyi Zhang, Arngeir Berge
Year: 2025
Publisher: Proceedings of the 27th International Conference on Multimodal Interaction

As generative AI becomes embedded in everyday life, understanding its broader social and relational impacts becomes increasingly important. This work introduces a practical design toolkit that helps researchers, designers, and students explore future human–AI relationships through scenario-building and critical reflection. The resulting framework encourages more responsible and context-aware approaches to emerging AI technologies.

Understanding and Shaping Human-Technology Assemblages in the Age of Generative AI

Authors: Josh Andres, Chris Danta, Andrea Bianchi, Sungyeon Hong, Zhuying Li, Eduardo Benitez Sandoval, Charles Patrick Martin, Ned Cooper
Year: 2024
Conference: 2024 ACM Designing Interactive Systems Conference

Generative AI should be understood not merely as a tool, but as part of larger human–technology–environment systems. Bringing together researchers and practitioners from diverse disciplines, the project explored methods for imagining and shaping possible futures of human–AI interaction. It laid the conceptual foundations for subsequent work on Human–GenAI relations and sociotechnical design.