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Susser D, Cabrera LY. Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy? AJOB Neurosci 2024; 15:122-133. [PMID: 37017379 DOI: 10.1080/21507740.2023.2188275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to "mental privacy." In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are-at least for now-no different from those raised by other well-understood data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum's "contextual integrity" theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts-healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy.
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Rainey S. Rights and Wrongs in Talk of Mind-Reading Technology. Camb Q Healthc Ethics 2024:1-11. [PMID: 38362894 DOI: 10.1017/s0963180124000045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
This article examines the idea of mind-reading technology by focusing on an interesting case of applying a large language model (LLM) to brain data. On the face of it, experimental results appear to show that it is possible to reconstruct mental contents directly from brain data by processing via a chatGPT-like LLM. However, the author argues that this apparent conclusion is not warranted. Through examining how LLMs work, it is shown that they are importantly different from natural language. The former operates on the basis of nonrational data transformations based on a large textual corpus. The latter has a rational dimension, being based on reasons. Using this as a basis, it is argued that brain data does not directly reveal mental content, but can be processed to ground predictions indirectly about mental content. The author concludes that this is impressive but different in principle from technology-mediated mind reading. The applications of LLM-based brain data processing are nevertheless promising for speech rehabilitation or novel communication methods.
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Affiliation(s)
- Stephen Rainey
- Philosophy and Ethics of Technology Section, TU Delft, Delft, The Netherlands
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Huang S, Paul U, Gupta S, Desai K, Guo M, Jung J, Capestany B, Krenzer WD, Stonecipher D, Farahany N. U.S. public perceptions of the sensitivity of brain data. J Law Biosci 2024; 11:lsad032. [PMID: 38259629 PMCID: PMC10800024 DOI: 10.1093/jlb/lsad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
As we approach an era of potentially widespread consumer neurotechnology, scholars and organizations worldwide have started to raise concerns about the data privacy issues these devices will present. Notably absent in these discussions is empirical evidence about how the public perceives that same information. This article presents the results of a nationwide survey on public perceptions of brain data, to inform discussions of law and policy regarding brain data governance. The survey reveals that the public may perceive certain brain data as less sensitive than other 'private' information, like social security numbers, but more sensitive than some 'public' information, like media preferences. The findings also reveal that not all inferences about mental experiences may be perceived as equally sensitive, and perhaps not all data should be treated alike in ethical and policy discussions. An enhanced understanding of public perceptions of brain data could advance the development of ethical and legal norms concerning consumer neurotechnology.
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Affiliation(s)
- Shenyang Huang
- Center for Cognitive Neuroscience, Duke University, Durham, North Carolina, USA
- Duke Initiative for Science & Society, Durham, North Carolina, USA
| | - Umika Paul
- Duke Initiative for Science & Society, Durham, North Carolina, USA
- University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Shikhar Gupta
- Duke Initiative for Science & Society, Durham, North Carolina, USA
| | - Karen Desai
- Duke Initiative for Science & Society, Durham, North Carolina, USA
- University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Melinda Guo
- Duke Initiative for Science & Society, Durham, North Carolina, USA
| | - Jennifer Jung
- University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | | | | | - Dylan Stonecipher
- Duke Initiative for Science & Society, Durham, North Carolina, USA
- Duke University, Durham, North Carolina, USA
| | - Nita Farahany
- Duke Initiative for Science & Society, Durham, North Carolina, USA
- Duke University, Durham, North Carolina, USA
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Ochang P, Eke D, Stahl BC. Corrigendum: Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse. Front Big Data 2023; 6:1344345. [PMID: 38169871 PMCID: PMC10758607 DOI: 10.3389/fdata.2023.1344345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
[This corrects the article DOI: 10.3389/fdata.2023.1240660.].
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Affiliation(s)
- Paschal Ochang
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, United Kingdom
| | - Damian Eke
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, United Kingdom
| | - Bernd Carsten Stahl
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, United Kingdom
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
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Andorno R, Lavazza A. How to deal with mind-reading technologies. Front Psychol 2023; 14:1290478. [PMID: 38034284 PMCID: PMC10682168 DOI: 10.3389/fpsyg.2023.1290478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Affiliation(s)
- Roberto Andorno
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zürich, Switzerland
| | - Andrea Lavazza
- Centro Universitario Internazionale, Arezzo, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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Ochang P, Eke D, Stahl BC. Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse. Front Big Data 2023; 6:1240660. [PMID: 38025947 PMCID: PMC10665841 DOI: 10.3389/fdata.2023.1240660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The study of the brain continues to generate substantial volumes of data, commonly referred to as "big brain data," which serves various purposes such as the treatment of brain-related diseases, the development of neurotechnological devices, and the training of algorithms. This big brain data, generated in different jurisdictions, is subject to distinct ethical and legal principles, giving rise to various ethical and legal concerns during collaborative efforts. Understanding these ethical and legal principles and concerns is crucial, as it catalyzes the development of a global governance framework, currently lacking in this field. While prior research has advocated for a contextual examination of brain data governance, such studies have been limited. Additionally, numerous challenges, issues, and concerns surround the development of a contextually informed brain data governance framework. Therefore, this study aims to bridge these gaps by exploring the ethical foundations that underlie contextual stakeholder discussions on brain data governance. Method In this study we conducted a secondary analysis of interviews with 21 neuroscientists drafted from the International Brain Initiative (IBI), LATBrain Initiative and the Society of Neuroscientists of Africa (SONA) who are involved in various brain projects globally and employing ethical theories. Ethical theories provide the philosophical frameworks and principles that inform the development and implementation of data governance policies and practices. Results The results of the study revealed various contextual ethical positions that underscore the ethical perspectives of neuroscientists engaged in brain data research globally. Discussion This research highlights the multitude of challenges and deliberations inherent in the pursuit of a globally informed framework for governing brain data. Furthermore, it sheds light on several critical considerations that require thorough examination in advancing global brain data governance.
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Affiliation(s)
- Paschal Ochang
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, United Kingdom
| | - Damian Eke
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, United Kingdom
| | - Bernd Carsten Stahl
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, United Kingdom
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
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Wang L, Yu L, Magnier B. Editorial: Deep learning for multimodal brain data processing and analysis. Front Neurosci 2023; 17:1264015. [PMID: 37662106 PMCID: PMC10471984 DOI: 10.3389/fnins.2023.1264015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/10/2023] [Indexed: 09/05/2023] Open
Affiliation(s)
- Liansheng Wang
- Department of Computer Science, Xiamen University, Xiamen, China
| | - Lequan Yu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Baptiste Magnier
- Euromov Digital Health in Motion, Mines-Telecom Institute Alès, Alès, France
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Brůha P, Mouček R, Salamon J, Vacek V. Workflow for health-related and brain data lifecycle. Front Digit Health 2022; 4:1025086. [PMID: 36532611 PMCID: PMC9748096 DOI: 10.3389/fdgth.2022.1025086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/01/2022] [Indexed: 09/19/2023] Open
Abstract
Poor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to change the current lifestyle. In conjunction with data representing human brain activity, we can obtain and identify human health problems resulting from a long-term lifestyle more precisely and, where appropriate, improve the quality and length of human life. Currently, brain and physical health-related data are not commonly collected and evaluated together. However, doing that is supposed to be an interesting and viable concept, especially when followed by a more detailed definition and description of their whole processing lifecycle. Moreover, when best practices are used to store, annotate, analyze, and evaluate such data collections, the necessary infrastructure development and more intense cooperation among scientific teams and laboratories are facilitated. This approach also improves the reproducibility of experimental work. As a result, large collections of physical and brain health-related data could provide a robust basis for better interpretation of a person's overall health. This work aims to overview and reflect some best practices used within global communities to ensure the reproducibility of experiments, collected datasets and related workflows. These best practices concern, e.g., data lifecycle models, FAIR principles, and definitions and implementations of terminologies and ontologies. Then, an example of how an automated workflow system could be created to support the collection, annotation, storage, analysis, and publication of findings is shown. The Body in Numbers pilot system, also utilizing software engineering best practices, was developed to implement the concept of such an automated workflow system. It is unique just due to the combination of the processing and evaluation of physical and brain (electrophysiological) data. Its implementation is explored in greater detail, and opportunities to use the gained findings and results throughout various application domains are discussed.
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Affiliation(s)
- Petr Brůha
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
| | - Roman Mouček
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
- New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
| | - Jaromír Salamon
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
| | - Vítězslav Vacek
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
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Rainey S, McGillivray K, Akintoye S, Fothergill T, Bublitz C, Stahl B. Is the European Data Protection Regulation sufficient to deal with emerging data concerns relating to neurotechnology? J Law Biosci 2020; 7:lsaa051. [PMID: 34386243 PMCID: PMC8355473 DOI: 10.1093/jlb/lsaa051] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/26/2020] [Accepted: 06/22/2020] [Indexed: 05/28/2023]
Abstract
Research-driven technology development in the fields of the neurosciences presents interesting and potentially complicated issues around data in general and brain data specifically. The data produced from brain recordings are unlike names and addresses in that it may result from the processing of largely involuntarily brain activity, it can be processed and reprocessed for different aims, and it is highly sensitive. Consenting for brain recordings of a specific type, or for a specific purpose, is complicated by these factors. Brain data collection, retention, processing, storage, and destruction are each of high ethical importance. This leads us to ask: Is the present European Data Protection Regulation sufficient to deal with emerging data concerns relating to neurotechnology? This is pressing especially in a context of rapid advancement in the fields of brain computer interfaces (BCIs), where devices that can function via recorded brain signals are expanding from research labs, through medical treatments, and beyond into consumer markets for recreational uses. One notion we develop herein is that there may be no trivial data collection when it comes to brain recording, especially where algorithmic processing is involved. This article provides analysis and discussion of some specific data protection questions related to neurotechnology, especially BCIs. In particular, whether and how brain data used in BCI-driven applications might count as personal data in a way relevant to data protection regulations. It also investigates how the nature of BCI data, as it appears in various applications, may require different interpretations of data protection concepts. Importantly, we consider brain recordings to raise questions about data sensitivity, regardless of the purpose for which they were recorded. This has data protection implications.
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