Can Machines Read our Minds?

Minds and Machines 29 (3):461-494 (2019)
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Abstract
We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual underpinnings, in order to help emphasise the relevant issues. To answer the question, we review a large number of empirical studies, in which samples of behaviour are used to automatically infer a range of psychological constructs, including affect and emotions, aptitudes and skills, attitudes and orientations (e.g. values and sexual orientation), personality, and disorders and conditions (e.g. depression and addiction). We also present a general perspective that can bring these disparate studies together and allow us to think clearly about their philosophical and ethical implications, such as issues related to consent, privacy, and the use of persuasive technologies for controlling human behaviour.
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2019
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First archival date: 2019-02-25
Latest version: 3 (2020-05-10)
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Presentation and Validation of the Radboud Faces Database.Langner, Oliver; Dotsch, Ron; Bijlstra, Gijsbert; Wigboldus, Daniel Hj; Hawk, Skyler T. & van Knippenberg, Ad

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Citations of this work BETA
On Social Machines for Algorithmic Regulation.Cristianini, Nello & Scantamburlo, Teresa
On Social Machines for Algorithmic Regulation.Cristianini, Nello & Scantamburlo, Teresa

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