Research on automated mental health assessment tools has been growing in recent years,
often aiming to address the subjectivity and bias that existed in the current clinical practice of …

There is limited research on fairness in automated decision-making systems in the clinical
domain, particularly in the mental health domain. Our study explores clinicians' perceptions …

In recent years, the affective computing research community has put ethics at the centre of its
research agenda. However, many of the currently available datasets for affective computing …

Unfair predictions of machine learning (ML) models impede their broad acceptance in real-
world settings. Tackling this arduous challenge first necessitates defining what it means for …

In recent years, the affective computing (AC) and human-robot interaction (HRI) research
communities have put fairness at the centre of their research agenda. However, none of the …

Unfair predictions of machine learning (ML) models impede their broad acceptance in real-
world settings. Tackling this arduous challenge first necessitates defining what it means for …

Social agents and robots are increasingly being used in wellbeing settings. However, a key
challenge is that these agents and robots typically rely on machine learning (ML) algorithms …

AI systems for depression detection on social media have been continuously improving their
performance, showing that meaningful patterns can be found in the data. While many …

Depression is a prevalent mental health disorder affecting both patients and society. The
ability to identify at-risk individuals early, accurately, and without human intervention can be …

Mental health illnesses cause significant suffering for individuals, their families, and society.
Early, accurate, and responsible detection of mental health problems is crucial for effective …