Background: The growing use of AI-based emotion recognition in organizational and digital environments has introduced new forms of socio-technical monitoring that may influence how individuals regulate emotional expression. However, the impact of AI-enabled emotion recognition on emotional self-regulation in human–system interaction remains insufficiently understood. Purpose: This study investigates how perceived surveillance in AI-enabled emotion recognition contexts influences expressive suppression. Specifically, it examines the mediating role of emotional self-censorship and the moderating role of privacy sensitivity. Research Design: The study adopts a moderated mediation research model to analyze the relationship between perceived surveillance and expressive suppression. Emotional self-censorship is proposed as a mediator, while privacy sensitivity is examined as a moderator. The analysis employs PROCESS Macro Model 8. Study Sample: The study sample consists of 300 adults who are familiar with AI-based emotion recognition technologies. Data Collection and/or Analysis: Survey data were collected from the participants and analyzed using PROCESS Macro Model 8 to test mediation and moderation effects. Results: The results indicate that perceived surveillance increases expressive suppression through emotional self-censorship. Furthermore, this indirect effect becomes stronger among individuals with high privacy sensitivity, indicating that privacy concerns intensify behavioral responses to AI monitoring. Conclusions: The findings reveal an unintended consequence of AI-based emotion recognition technologies: they may reinforce emotional self-censorship and reduce authenticity in emotional expression. The study contributes to understanding human behavior in AI-mediated environments and provides managerial and policy implications for developing privacy-aware and human-centered AI systems that promote sustainable interaction and psychological well-being.
AhmedBBarkerDFarhanA. Emotion recognition from facial expressions and speech: a review of datasets and techniques. Int J Hum Comput Interact2021; 37(15): 1423–1440. https://doi.org/10.1080/10447318.2021.1882340
2.
GrossJJJohnOP. Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. J Pers Soc Psychol2003; 85(2): 348–362. https://doi.org/10.1037/0022-3514.85.2.348
3.
MinkkinenMZimmerMFloridiL. Human agency in AI-mediated environments: toward a socio-technical understanding of algorithmic influence. Ethics Inf Technol2024; 26(2): 1–15. https://doi.org/10.1007/s10676-024-09715-2
4.
TyraATFergusTAGintyAT. Emotion suppression and acute physiological responses to stress in healthy populations: a quantitative review of experimental and correlational investigations. Health Psychol Rev2024; 18(2): 396–420. https://doi.org/10.1080/17437199.2023.2264102
5.
ZuboffS. The age of surveillance capitalism: the fight for a human future at the new frontier of power. PublicAffairs, 2019.
6.
UguzS. Surveillance capitalism, affective computing, and emotional labor: reconfiguring control in the workplace. AI Soc2025; 40(2): 221–239. https://doi.org/10.1007/s00146-025-01579-2
7.
Bar-TalD. Self-censorship as a socio-political-psychological phenomenon: conception and research. Adv Polit Psychol2017; 38(1): 37–65. https://doi.org/10.1111/pops.12391
BélangerFCrosslerRE. Privacy in the digital age: a review of information privacy research in information systems. MIS Q2011; 35(4): 1017–1041. https://doi.org/10.2307/41409971
11.
GillamSJainN. Human-centered accountability in AI governance: rethinking agency and responsibility in algorithmic systems. AI Ethics2024; 4(1): 55–72. https://doi.org/10.1007/s43681-024-00312-7
12.
VinuesaRAzizpourHLeiteI, et al.The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications2020; 11(1): 233. https://doi.org/10.1038/s41467-019-14108-y
13.
SaavedraAChocarroRCortinasM, et al.Exploring unconscious user responses to affective computing in interactive prototypes: a consumer neuroscience study. Behav Inf Technol2025; 44(1): 66–82. https://doi.org/10.1080/0144929X.2025.2005837
14.
BrehmJW. A theory of psychological reactance. Academic Press, 1966.
15.
LyonD. The culture of surveillance: watching as a way of life. Polity Press, 2018.
16.
HochschildAR. The managed heart: Commercialization of human feeling. University of California Press, 1983.
17.
FariaEPintoAOliveiraS, et al.Collaborative robots and user trust: the role of saccadic gaze, anthropomorphic motion, and repetitive training. Computers in Human Behavior Reports2026; 21: 100938. https://doi.org/10.1016/j.chbr.2026.100938
18.
SestinoAD’AngeloA. My doctor is an avatar! The effect of anthropomorphism and emotional receptivity on individuals’ intention to use digital-based healthcare services. Technol Forecast Soc Change2023; 191: 122505. https://doi.org/10.1016/j.techfore.2023.122505
19.
SelsLOverallNCLoeysT, et al.The use and consequences of expressive suppression in high-risk and low-risk relationship discussions. Emotion2025; 25(5): 1293–1305. https://doi.org/10.1037/emo0001196
20.
SchneierB. Click here to kill everybody: Security and survival in a hyper-connected world. W. W. Norton & Company, 2018.
21.
FranchowEISuchyY. Expressive suppression and executive functioning: The moderating role of subjective cognitive complaints. Personality and Individual Differences2014; 66: 44–49. https://doi.org/10.1016/j.paid.2014.03.003
22.
HayesAF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. 2nd ed.Guilford Press, 2018.
