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Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care
The objectives were to: 1) Collect audio data from 24 people (12 with depression and 12 without mental health or major health condition diagnoses); 2) Build a machine learning model to predict depression risk.
Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?
Our mapping of what prevents workers from meaningfully consenting to workplace wellbeing technologies (challenges) and what they require to do so (interventions) illustrates how the lack of meaningful consent is a structural problem requiring socio-technical solutions.
Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect
The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors.
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
Utilizing two case studies in distinct domains, we empirically derive a framework that facilitates systematic charting of the sociotechnical gap by connecting AI guidelines in the context of XAI and elucidating how to use them to address the gap.
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
In this paper, we create a benchmark for predictive analysis of individuals from a perspective that integrates: physical and physiological behavior, psychological states and traits, and job performance.
