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Journal of Law and the Biosciences
Article . 2022 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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PubMed Central
Article . 2022
License: CC BY NC ND
Data sources: PubMed Central
SSRN Electronic Journal
Article . 2021 . Peer-reviewed
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Mental Data Protection and the GDPR

Authors: Marcello Ienca; Gianclaudio Malgieri;

Mental Data Protection and the GDPR

Abstract

Abstract Although decoding the content of mental states is currently unachievable, technologies such as neural interfaces, affective computing systems, and digital behavioral technologies enable increasingly reliable statistical associations between certain data patterns and mental activities such as memories, intentions, and emotions. Furthermore, Artificial Intelligence enables the exploration of these activities not just retrospectively but also in a real-time and predictive manner. In this article, we introduce the notion of ‘mental data’, defined as any data that can be organized and processed to make inferences about the mental states of a person, including their cognitive, affective and conative states. Further, we analyze existing legal protections for mental data by considering the lawfulness of their processing in light of different legal bases and purposes, with special focus on the EU General Data Protection Regulation (GDPR). We argue that the GDPR is an adequate tool to mitigate risks related to mental data processing. However, we recommend that interpreters focus on processing characteristics, rather than merely on the category of data at issue. Finally, we call for a ‘Mental Data Protection Impact Assessment’, a specific data protection impact assessment designed to better assess and mitigate the risks to fundamental rights and freedoms associated with the processing of mental data.

Keywords

Original Article

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    49
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
49
Top 1%
Top 10%
Top 10%
Green
gold