Affect-Aware Machine Learning Models for Deception Detection

Authors

  • Leena Mathur Department of Computer Science, Viterbi School of Engineering, University of Southern California

Keywords:

Activity Recognition, Machine Learning, Machine Perception, Human Computer Interaction, Applications Of AI

Abstract

Automated deception detection systems can enhance societal well-being by helping humans detect deceivers and support people in high-stakes situations across health, social work, and legal domains. Existing computational approaches for detecting deception have not leveraged dimensional representations of affect, specifically valence and arousal, expressed during communication. My research presents a novel analysis of the potential for including affect in machine learning models for detecting deception. My work informs and motivates the development of affect-aware machine learning approaches for modeling deception and other social behaviors during human interactions in-the-wild. This research, independently defined and conducted by me, is from work-in-progress towards my undergraduate thesis in the Department of Computer Science at the University of Southern California.

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Published

2021-05-18

How to Cite

Mathur, L. (2021). Affect-Aware Machine Learning Models for Deception Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15968-15969. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17980

Issue

Section

AAAI Undergraduate Consortium