Ethical Governance of Emotion-Recognition Technologies in Public Spaces

Elena Fischer

Ethical Governance of Emotion-Recognition Technologies in Public Spaces

Author(s)	Dr. Elena Fischer
Country	United States
Abstract	Emotion-recognition technologies are increasingly positioned as tools capable of interpreting human affect from facial movements, voice characteristics, posture, physiological signals, and other behavioral indicators. Their proposed deployment in transportation facilities, shopping environments, stadiums, civic buildings, health-service entrances, and other publicly accessible spaces creates governance challenges that are qualitatively different from those associated with voluntary consumer applications. Individuals moving through public environments may have limited practical ability to avoid sensing infrastructure, may not understand that emotional states are being inferred, and may be unable to challenge inaccurate classifications. At the same time, scientific debate continues over the reliability of inferring internal emotional states directly from observable facial behavior, especially when cultural, situational, linguistic, and interpersonal context is insufficiently represented. Barrett and colleagues have emphasized that facial movements cannot consistently be treated as universal diagnostic indicators of particular emotions, strengthening the case for caution when such inferences influence consequential decisions. This study develops an ethical-governance framework for emotion-recognition technologies operating in public spaces. The research adopts a conceptual-methodological design supported by structured policy analysis and a transparent simulation-based risk assessment. Six deployment settings are evaluated using six governance dimensions: privacy intrusion, meaningful consent limitations, inference-validity uncertainty, demographic and contextual bias, function-creep potential, and accountability weakness. The analysis indicates that high-density and unavoidable environments, particularly transportation hubs, may generate especially elevated ethical risk because surveillance scale, low practical opt-out capacity, and possible security-related consequences interact simultaneously. The paper proposes a governance architecture based on necessity, proportionality, purpose limitation, data minimization, independent validation, public transparency, human review, contestability, and continuous auditing. The proposed model treats emotion recognition not merely as a technical classification problem but as a form of probabilistic behavioral inference requiring heightened institutional responsibility.
Keywords	emotion recognition, affective computing, public spaces, artificial intelligence ethics, biometric governance, algorithmic accountability, privacy, surveillance, human rights, responsible AI
Field	Engineering
Published In	Volume 2, Issue 6, November-December 2021
Published On	2021-11-02

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American Journal of Advanced Multidisciplinary Innovation and Research

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Ethical Governance of Emotion-Recognition Technologies in Public Spaces

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