Physiological Feedback Loops

Definition

Physiological Feedback Loops examines closed loops in which AI changes behaviour using real-time biological signals within the broader domain of the preservation of biological safety, homeostasis, identity and agency during sustained AI-mediated interaction.

Why this matters

Failures concerning closed loops in which AI changes behaviour using real-time biological signals can cause physical or psychological harm, loss of function, privacy invasion, identity compromise, exclusion, coercion or irreversible impact on human agency.

Research questions

Under which conditions can closed loops in which AI changes behaviour using real-time biological signals be delivered, measured or protected reliably, and how do outcomes vary by person, device, duration, context and governance controls?

Hypotheses

A transparent, safety-bounded and person-centred approach to physiological feedback loops, combining validated measurement, informed consent, privacy and security controls, human oversight and longitudinal monitoring, will improve benefit–risk outcomes compared with opaque or technology-centred approaches.

Proposed methods

physiological monitoring; controlled exposure studies; ecological momentary assessment; longitudinal cohort follow-up; stress and workload testing; embodiment studies; systems safety analysis; expert and affected-user review; reproducibility testing; methods adapted specifically to Physiological Feedback Loops

Stakeholders and beneficiaries

AI users; workers; patients; clinicians; occupational health teams; employers; regulators; device manufacturers; researchers; insurers