Automatic emotion recognition (ER)-enabled wellbeing interventions use ER algorithms to
infer the emotions of a data subject (ie, a person about whom data is collected or processed …

Getting training data for machine learning (ML) prediction of mental illness on social media
data is labor intensive. To work around this, ML teams will extrapolate proxy signals, or …

Purpose In this paper, we introduce feminist technoscience as an approach that will
advance theory in information behavior and practice. Design/methodology/approach In this …

This article builds on the argument that feminist technoscience will advance information
practice scholarship beyond its current limitations. These limitations reflect neoliberalism in …

Digital mental health is often touted as a solution to issues of access to mental health care.
However, there has been little research done to understand the accessibility of digital mental …

Consumer mental health apps (MHAs) collect and generate mental health-related data on
their users, which can be leveraged for research and product improvement studies. Such …

Emotion AI, or AI that claims to infer emotional states from various data sources, is
increasingly deployed in myriad contexts, including mental healthcare. While emotion AI is …

This chapter examines the rise of biometric monitoring using AI and other automated
decision systems in the disability and mental health context. It focuses on the use of “digital …

Research shows that college students are largely unaware of the impact of algorithms on
their everyday lives. Also, most university students are not being taught about algorithms as …

Mobile phones are probably one of the fastest growing and most rapidly adopted
technologies in the world. The various apps and their health features are still relatively new …