> For the complete documentation index, see [llms.txt](https://library.xrguild.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://library.xrguild.org/neurotechnology-brain-tech-and-ethical-challenges/u.s.-public-perceptions-of-the-sensitivity-of-brain-data.md).

# U.S. Public Perceptions of the Sensitivity of Brain Data

## Overview

**Authors:** Huang, S., Paul, U., Gupta, S., Desai, K., Guo, M., Jung, J., Capestany, B. H., Krenzer, W. D., Stonecipher, D., Farahany, N. A.

**Publication Date:** 16 July 2025

**Link:** <https://academic.oup.com/jlb/article/11/1/lsad032/7577652?login=false>

**Keywords:** Bioethics, data privacy, neurotechnologies brain data governance, public perceptions

**Type:** Peer-Reviewed Journals/White Papers

## Summary

This article addresses the emerging concerns surrounding data privacy in consumer neurotechnology. Through a nationwide survey, it examines public perceptions of brain data to contribute empirical evidence to discussions on law and policy regarding brain data governance. The survey findings suggest that the public may view certain brain data as less sensitive than traditional private information like social security numbers but more sensitive than some public information such as media preferences. Additionally, not all inferences about mental experiences are perceived equally sensitive, indicating a nuanced approach is needed in ethical and policy discussions. Enhanced understanding of public perceptions regarding brain data can inform the development of ethical and legal norms concerning consumer neurotechnology, bridging the gap between societal expectations and regulatory frameworks.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://library.xrguild.org/neurotechnology-brain-tech-and-ethical-challenges/u.s.-public-perceptions-of-the-sensitivity-of-brain-data.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.