Digital mental health applications promise scalable and cost-effective solutions to mitigate the gap between the demand and supply of mental healthcare services. However, very little attention is paid on differential impact and potential discrimination in digital mental health services with respect to different sensitive user groups (e.g., race, age, gender, ethnicity, socio-economic status) as the extant literature as well as the market lack the corresponding evidence. In this paper, we outline a 7-step model to assess algorithmic discrimination in digital mental health services, focusing on algorithmic bias assessment and differential impact. We conduct a pilot analysis with 610 users of the model applied on a digital wellbeing service called Foundations that incorporates a rich set of 150 proposed activities designed to increase wellbeing and reduce stress.
