Poster: The Haunting Problem — Recognizing Semantic Absence in Trauma-Aware AI

This poster summarizes the theoretical foundation of The Haunting Problem, introducing semantic absence as a new class of verification failure in trauma-aware AI. It visualizes how emotional silence, suppression, and dissociation—often overlooked by conventional affective models—can be represented as formal states within a verifiable system. The work outlines how absence itself becomes a detectable and ethically meaningful signal, bridging multimodal affective data (language, audio, and microexpressions) with symbolic verification through Z3 satisfiability logic. This visualization accompanies the full preprint: The Haunting Problem: Recognizing Semantic Absence in Trauma-Aware AI (George, 2025), available at https://doi.org/10.5281/zenodo.17578153

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-11
DOI
https://doi.org/10.5281/zenodo.17584658
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00
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article

Poster: The Haunting Problem — Recognizing Semantic Absence in Trauma-Aware AI

Zenodo (CERN European Organization for Nuclear Research)
Emotion and Mood Recognition
article

Poster: The Haunting Problem — Recognizing Semantic Absence in Trauma-Aware AI

article en

Abstract

This poster summarizes the theoretical foundation of The Haunting Problem, introducing semantic absence as a new class of verification failure in trauma-aware AI. It visualizes how emotional silence, suppression, and dissociation—often overlooked by conventional affective models—can be represented as formal states within a verifiable system. The work outlines how absence itself becomes a detectable and ethically meaningful signal, bridging multimodal affective data (language, audio, and microexpressions) with symbolic verification through Z3 satisfiability logic. This visualization accompanies the full preprint: The Haunting Problem: Recognizing Semantic Absence in Trauma-Aware AI (George, 2025), available at https://doi.org/10.5281/zenodo.17578153

Zenodo (CERN European Organization for Nuclear Research)
Vanderbilt University (US)
Reduced inequalities
Openalex Percentile: Top 39%
Emotion and Mood Recognition
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Poster: The Haunting Problem — Recognizing Semantic Absence in Trauma-Aware AI · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS