Fiduciary AI for the Future of Brain-Technology Interactions

TL;DR

This paper proposes embedding fiduciary duties into brain-computer interface AI models to ensure ethical use, protect user interests, and mitigate risks associated with neural data interpretation.

Contribution

It introduces a novel framework for integrating legal fiduciary principles into brain foundation models through technical and governance mechanisms.

Findings

Fiduciary principles can be operationalized in AI design.

Architectural mechanisms for fiduciary AI are feasible.

Fiduciary AI enhances user trust and safety.

Abstract

Brain foundation models represent a new frontier in AI: instead of processing text or images, these models interpret real-time neural signals from EEG, fMRI, and other neurotechnologies. When integrated with brain-computer interfaces (BCIs), they may enable transformative applications-from thought controlled devices to neuroprosthetics-by interpreting and acting on brain activity in milliseconds. However, these same systems pose unprecedented risks, including the exploitation of subconscious neural signals and the erosion of cognitive liberty. Users cannot easily observe or control how their brain signals are interpreted, creating power asymmetries that are vulnerable to manipulation. This paper proposes embedding fiduciary duties-loyalty, care, and confidentiality-directly into BCI-integrated brain foundation models through technical design. Drawing on legal traditions and recent advancements in AI alignment techniques, we outline implementable architectural and governance mechanisms to ensure these systems act in users' best interests. Placing brain foundation models on a fiduciary footing is essential to realizing their potential without compromising self-determination.