Art installation reveals problems in emotion ai face reading
The Body-Algorithm-Emotion Loop: Making Emotion AI Misrecognition Visible through Artistic Practice
Human-Computer Interaction
Summary
Emotion AI tries to read people’s feelings from their facial expressions, but it often gets these emotions wrong. The authors created an interactive art installation that shows how this technology misclassifies faces and emotions. Instead of just explaining the technology clearly, their work makes people physically feel how their faces are judged and labeled incorrectly. This helps make the hidden biases in emotion AI more visible and open to discussion.
What this means in practice
- •For interactive exhibit designers: Create installations that reveal and critique AI biases by physically engaging audiences with misrecognition experiences.
- •For ai ethics teams: Use embodied artistic representations to communicate the limits and harms of emotion recognition AI to stakeholders and policymakers.
A position paper. It proposes an approach and reports no results.
Authors
Chunyi Shen
Abstract
This pictorial presents The Body in Algorithmic Colonialism, a screen-based interactive installation that critically examines Emotion-AI through artistic practice. The work uses facial tracking to simulate how commercial Emotion-AI systems translate facial movement into emotional categories and verdicts of compliance or non-compliance. Rather than treating explainability as technical transparency alone, the installation approaches XAI as an embodied encounter with misrecognition: viewers experience how the body is measured, classified, and returned to itself through algorithmic thresholds. Drawing on critical AI studies, affect theory, and practice-as-research, the pictorial positions artistic practice as a method for making affective classification visible and contestable. It documents the conceptual framework, technical process, and critical context of a prototype developed in the first year of a practice-based PhD. No audience responses, interviews, or participant data are analysed in this submission.