4 accepted papers
Multimodal Self-Supervised Learning (SSL) has achieved remarkable success by learning representations from multiple views of data. However, prevalent methods rely on the redundancy assumption—that different views share substantial task-relevant information. We argue that this assumption fails in com…
Using intelligent systems to perceive psychological and social behaviors, that is, the underlying affective, cognitive, and pathological states that are manifested through observable behaviors and social interactions, remains a challenge due to their complex, multifaceted, and personalized nature. E…
Machine learning (ML) bias in mental health detection and analysis is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multimodal methods work better than unimodal methods, there is minimal work on multimodal fairness for depression detection. We propose a ca…
Mental health is becoming an increasingly prominent health challenge. Despite a plethora of studies analysing and mitigating bias for a variety of tasks such as face recognition and credit scoring, research on machine learning (ML) fairness for mental health has been sparse to date. In this work, we…