This design research interrogates the evolving paradigms of human-intelligent machine interactions, anchored in the biological principles of symbiosis. The thesis scrutinises the dynamic interplay between humans and technology, particularly in the context of the pervasive integration of intelligent machines into the multifaceted dimensions of daily life, which precipitates both novel challenges and opportunities. The investigation aims to elucidate the foundational elements that could incentivise these interactions and examines how researchers and designers might conceptualise a more profound symbiotic link between humans and intelligent machines. This doctoral inquiry probes the potential of leveraging symbiotic relationships observed in nature as a cornerstone for the advancement of machine intelligence systems, with the objective of augmenting human-machine interfaces. By establishing analogies between biological symbiosis—encompassing mutualism, parasitism, commensalism, and amensalism—and artificial intelligence (AI), the study endeavours to transpose these natural dynamics onto the design of intelligent machines that bolster human capabilities and contribute to societal well-being.

The thesis is propelled by three principal research questions: (1) How can symbiotic relationships observed in nature serve as a muse for the design of machine intelligence systems? (2) How can the principles of biological symbiosis be harnessed to fortify human-machine collaborations? (3) In what manner can symbiotic design principles be applied to overcome extant limitations within Artificial Intelligence algorithms and systems? Employing a structured methodology that covers environment comprehension and the proposition of innovative approaches, this doctoral thesis seeks to chart a forward-looking trajectory for Artificial Intelligence research and application, deeply rooted in the principles of symbiosis.

A practice-oriented research methodology unfolds across three distinct project branches. The initial branch delves into the experiential journey of a Machine Learning engineer, shedding light on the pragmatic facets of Artificial Intelligence development. The subsequent branch undertakes the categorisation of machine intelligence from a symbiotic perspective, advocating for a novel taxonomy for Artificial Intelligence systems. The final branch critically evaluates the ramifications of the symbiotic framework on privacy and surveillance within human-AI systems, underscoring the ethical and practical dilemmas of integration.

This dissertation highlights a design philosophy predicated on mutual enhancement and co-evolution between humans and machines, arguing that intelligent machines inspired by biological symbiosis can generate more adaptive, resilient, and ethically robust technologies. Focusing on an audience across the disciplines of Artificial Intelligence, design, and biological sciences, this work advocates for a paradigm where technology and humanity coalesce, guided by the knowledge stemming from natural symbiosis.