Emotional AI, soft biometrics and the surveillance of emotional life: An unusual consensus on privacy
Open accessAbstract
By the early 2020s, emotional artificial intelligence (emotional AI) will become increasingly present in everyday objects and practices such as assistants, cars, games, mobile phones, wearables, toys, marketing, insurance, policing, education and border controls. There is also keen interest in using these technologies to regulate and optimize the emotional experiences of spaces, such as workplaces, hospitals, prisons, classrooms, travel infrastructures, restaurants, retail and chain stores. Developers frequently claim that their applications do not identify people. Taking the claim at face value, this paper asks, what are the privacy implications of emotional AI practices that do not identify individuals? To investigate privacy perspectives on soft non-identifying emotional AI, the paper draws upon the following: over 100 interviews with the emotion detection industry, legal community, policy-makers, regulators and NGOs interested in privacy; a workshop with stakeholders to design ethical codes for using data about emotions; a UK survey of 2068 citizens on feelings about emotion capture technologies. It finds a weak consensus among social stakeholders on the need for privacy, this driven by different interests and motivations. Given this weak consensus, it concludes that there exists a limited window of opportunity to societally agree principles of practice regarding privacy and the use of data about emotions.
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Interaction histories can serve as proxies for emotional regulation or mental-health status.
“Interaction histories can serve as proxies for emotional regulation or mental-health status [109, 110]”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [109]
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Emotion AI in Job Interviews: Injustice, Emotional Labor, Identity, and Privacy
Ingber, A. S., & Andalibi, N. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732002 · Cited by 5 · see citing works
Historical Methods for AI Evaluations, Assessments, and Audits
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Generative Artificial Intelligence in University Education
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- Authors
- McStay, A.
- Affiliations
- Bangor University (GB)
- Corresponding author
- McStay, Andrew
- ORCID
- McStay, Andrew
- Venue
- Big Data & Society
- Publisher
- SAGE Publications
- Published
- 2020-01
- Volume
- 7
- Issue
- 1
- Pages
- 205395172090438
- ISSN
- 2053-9517 (ISSN-L)
- OpenAlex
- W3013109186
- Cited by
- 217
- References
- 25
- Citations by year
- 2026: 41 · 2025: 56 · 2024: 47 · 2023: 28 · 2022: 24 · 2021: 17 · 2020: 3 · 2019: 1
- FWCI
- 24.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Big Data & Society (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Bangor University Research Portal (Bangor University) (submittedVersion, other-oa)
- Funders
- Economic and Social Research Council (ES/T00696X/1)
- Arts and Humanities Research Council (AH/M006654/1)
- Engineering and Physical Sciences Research Council (EP/R045178/1)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- emotional artificial intelligence, soft biometrics, emotion recognition, privacy, stakeholder consensus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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