November 25 - 27, 2019
Closing the regulatory gap for consumer neurotechnology
Monday, November 25, 2019
Day 1: Neurotechnology, consumer applications and conceptual foundations
10:00 - 12:00 – Informal welcome at Brocher Foundation of arriving participants and registration
12:00 - 12:15 – Introduction and presentation of the workshop by the organizers
Session 1 - Chair: Ralf Jox
12:15 - 12:35 – Tonio Ball (Uni Freiburg): State of the Art Neurotechnology
12:35 - 12:55 – Fabrice Jotterand (Medical College of Wisconsin / Uni Basel): Consumer
Neurotechnology and Our Incumbent Anthropological Identity Crisis
12:55 - 13:15 – Orsolya Friedrich (FernUni Hagen): Implications of Consumer Neurotechnology
for Autonomy and Agency
13:15 - 14:00 – Lunch
Session 2 - Chair: Fabrice Jotterand
14:00 - 14:30 – Rafael Yuste (Columbia University) [via Videocall]: Neurorights and the Technocratic Oath
14:30 – 15:30 – Group Discussion (telepresence of Rafael Yuste) and presentation of break-out groups.
15:30 – 15:50 – Coffee break
15:50 - 16:10 –Philipp Kellmeyer (Uni Freiburg): Ethics of Machine Learning and Brain Data Analytics
16:10 – 18:00 – Break-out groups (16:10-17:30) and plenary group presentations (17:30-18:00)
19:00 – 21:00 – Dinner
Tuesday, November 26, 2019
Day 2: Brain Data from Consumer Neurotechnology and the Need for Legal and Technical Standards
Session 1 - Chair: Silja Vöneky
9:00 - 9:20 – Fruzsina Molnár-Gábor (Heidelberg Academy of Sciences, Heidelberg):
Brain Data and Genetic Data: A comparative legal analysis
9:20 - 9:40 – Marcello Ienca (ETH Zurich): Brain Data, Mental Information and Neurorights: the Need for Adaptive and Multi-level Governance
9:40 - 10:00 – Silja Vöneky (Uni Freiburg): The Legal Boundaries of Neurotechnology
10:00 - 10:20 – Coffee break
10:20 - 10:40 – Joseph J. Fins (Weill Cornell Medical College): Regulating Neurotechnology a
10:40 - 11:00 – Group discussion on presentations
11:00 – 12:30 – Collective writing of draft guideline document (Part I): Writing down of results from group discussions, logistics, and delineation of first steps
12:30 - 13:30 – Lunch
Session 2 - Chair: Marcello Ienca
13:30 - 13:50 – Ralf J. Jox (Uni Lausanne): What is Specific About Brain Data that Warrants
13:50 - 14:10 – Ricardo Chavarriaga (EPFL): Developing Standards for Neurotechnology and
14:10 - 14:30 – James Scheibner (ETH Zurich): Balancing Data Protection and Technical Solutions: Insights from the DPPH Project
14:30 - 14:50 – Claude Castelluccia (INRIA): Cognitive Security
14:50 - 15:10 – Coffee break
15:10 - 16:30 – Group Discussion & Working Groups: Discussing the afternoon talks and the
results of the first round of drafting
16:30 - 17:00 – Hank Greely (Stanford) [via Videocall]: Neuroethics & International Brain Data Governance
17:00 - 18:00 – Collective writing of draft guideline document (Part II): Format, structure and identification of priorities
19:00 – Dinner
Wednesday, November 27, 2019
Day 3: Closing the Regulatory Gap
Session 1 - Chair: Philipp Kellmeyer
9:00 - 9:20 – Roberto Andorno (Uni Zurich): Brain Data and Human Rights
9:20 - 9:40 – Hervé Chneiweiss (INSERM): Neurotechnologies and Identity: from Games to
Bias? Ethical Issues Between Preserving Autonomy and Preventing Vulnerability
9:40 - 10:00 – Jean-Marc Rickli (GCSP): Security Issues of AI and Neurotechnology
10:40 - 11:00 – Coffee Break
11:00 - 13:00 – Break-out groups and collective writing of draft guideline document (Part III): Content, timeline and implementation
13:00 - 14:00 – Lunch
14:00 - 15:30 – Plenary session on guidelines development & definition of next steps
15:30 – Farewell
- Kellmeyer Philipp, University of Freiburg Medical Center, Department of Neurosurgery & Freiburg Institute for Advanced Studies (FRIAS), Researcher
- Vöneky Silja
- Ienca Marcello
- Jox Ralf
- Jotterand Fabrice
The combination of big data and advanced machine learning—often referred to as a form of artificial intelligence (AI) or “intelligent systems”—may increase the efficiency and accuracy of automated systems and of economic processes and is therefore of great interest to industries across all sectors, especially health care and medical technology (Mittelstadt et al., 2016; Echeverría and Tabarés, 2017).
In the area of medical technology, concomitant progress in microsystems engineering has turbocharged the field of intelligent neurotechnology, i.e. devices for decoding brain data for clinical, consumer or military applications. Big information technology and software companies, as well as many “neurotech” startups, are now actively developing neurotechnological systems directly targeted at consumers, often for “paramedical” applications, for example neurofeedback for relieving stress or anxiety or for brain stimulation (Ienca et al., 2018; Kellmeyer, 2018; Wexler, 2017; Piwek et al., 2016). As these devices and applications typically fall outside of medical device regulation regimes, a growing (grey) market of direct-to-consumer (DTC) neurotechnology is emerging that creates a number of ethical, legal, social and political challenges (Kellmeyer, 2018; Ienca et al., 2018; Yuste et al., 2017).
With regard to intelligent neurotechnologies, scholars have recently raised ethical concerns and indicated issues of privacy, agency, identity, data security, human enhancement, algorithmic biases and discrimination as the main ethical, legal and sociopolitical challenges in this domain (Kellmeyer, 2018, Yuste et al., 2017, Jotterand & Ienca 2017). In particular, intelligent neurotechnological devices raise concerns regarding control and responsibility, e.g. in terms of gaps in moral and legal accountability in cases in which decision-making capacity is relegated from human users to a device (or software-based decision-support system), for example in an intelligent brain-computer interface (Kellmeyer et al., 2016; Grübler, 2011). In terms of conceptual philosophical foundations, neurotechnological devices that interact closely with individuals, such as brain-computer interfaces for medical or entertainment purposes, also challenge concepts of agency, autonomy and identity (Friedrich et al., 2018; Kellmeyer et al., 2016; Jotterand 2016; Gilbert, 2015). From the perspective of legal studies and in terms of regulatory guidance, the legitimacy of using intransparent algorithms in safety-critical applications is questioned (Voeneky and Neuman, 2018). Furthermore, algorithm-based evidence might be inconclusive, inscrutable, or misguided and therefore pose significant barriers for establishing trust between human users and the intelligent device (Kellmeyer et al., 2018; Gaudiello et al., 2016; Battaglia et al., 2014).
Especially the collection of large amounts of brain data in the hands of private companies raises concerns about the security of these data from unwarranted access and misuse. The recent case of data abuse by Facebook has raised awareness for the general risks associated with the acquisition and storage of large quantities of personal data. Particularly, it is unclear whether existing legal frameworks for data protection and governance suffice in protecting consumers from these effects (Ienca et al., 2018; Kellmeyer, 2018). Apart from individual privacy, especially mental privacy, the ability of advanced machine learning algorithms to learn on aggregated data collected from many individuals also raises questions on group privacy (Taylor et al., 2017) as well as questions on the privacy of first-person subjective experience (“mental privacy”). Among other sequelae, these concerns have spawned a debate on the moral and legal status of mental states, e.g. the question whether the right to mental privacy should be framed in the context of human rights (Ienca and Andorno, 2017).
This project aims at addressing these concerns by engaging in multidisciplinary reflections to examine philosophical, ethical, legal and social challenges arising intelligent neurotechnologies, specifically in the context of consumer applications.
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