> 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 This article addresses the emerging concerns surrounding data privacy in consumer neurotechnology. ## 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:** **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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://library.xrguild.org/neurotechnology-brain-tech-and-ethical-challenges/u.s.-public-perceptions-of-the-sensitivity-of-brain-data.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.