mlsanityData to 7 Oct 2026

The Nonverbal Gap: Toward Affective Computer Vision for Safer and More Equitable Online Dating

arXiv 2603.26727 ↗First posted 20 Mar 2026cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Online dating has become the dominant way romantic relationships begin, yet current platforms strip the nonverbal cues: gaze, facial expression, body posture, response timing, that humans rely on to signal comfort, disinterest, and consent, creating a communication gap with disproportionate safety consequences for women. We argue that this gap represents both a technical opportunity and a moral responsibility for the computer vision community, which has developed the affective tools, facial action unit detection, gaze estimation, engagement modeling, and multimodal affect recognition, needed to begin addressing it, yet has largely ignored the dating domain as a research context. We propose a fairness-first research agenda organized around four capability areas: real-time discomfort detection, engagement asymmetry modeling between partners, consent-aware interaction design, and longitudinal interaction summarization, each grounded in established CV methodology and motivated by the social psychology of romantic communication. We argue that responsible pursuit of this agenda requires purpose-built datasets collected under dyadic consent protocols, fairness evaluation disaggregated across race, gender identity, neurotype, and cultural background, and architectural commitments to on-device processing that prevent affective data from becoming platform surveillance infrastructure. This vision paper calls on the WICV community, whose members are uniquely positioned to understand both the technical opportunity and the human stakes, to establish online dating safety as a first-class research domain before commercial deployment outpaces ethical deliberation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 99 references that are in the map. First posted on arXiv 20 Mar 2026.

Builds on

13 of 99 references.

References · 13

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  3. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  4. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
  5. 2019Gaze360: Physically Unconstrained Gaze Estimation in the Wildcited by 173
  6. 2015Appearance-Based Gaze Estimation in the Wildcited by 153
  7. 2018A generic framework for privacy preserving deep learningcited by 103
  8. 2020Investigating Bias and Fairness in Facial Expression Recognitioncited by 87
  9. 2020Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Researchcited by 67
  10. 2019SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wildcited by 62
  11. 2021Py-Feat: Python Facial Expression Analysis Toolboxcited by 33
  12. 2016Instagram photos reveal predictive markers of depressioncited by 25
1 more
  1. 2016A First Look at User Activity on Tindercited by 2

Built on by

Not cited yet.