4 papers
Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches
Obed Junias, Prajakta Kini, Theodora Chaspari
This paper investigates algorithmic bias in language-based models for automated depression detection, focusing on socio-demographic disparities related to gender and race/ethnicity…
Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions
Ankush Raut, Projna Paromita, Sydney Begerowski +2
We explore the feasibility of large language models (LLMs) in detecting subtle expressions of micro-behaviors in team conversations using transcripts collected during simulated spa…
Robust and Explainable Depression Identification from Speech Using Vowel-Based Ensemble Learning Approaches
Kexin Feng, Theodora Chaspari
This study investigates explainable machine learning algorithms for identifying depression from speech. Grounded in evidence from speech production that depression affects motor co…
A Pilot Study on Clinician-AI Collaboration in Diagnosing Depression from Speech
Kexin Feng, Theodora Chaspari
This study investigates clinicians' perceptions and attitudes toward an assistive artificial intelligence (AI) system that employs a speech-based explainable ML algorithm for detec…