Understanding Fairness in Data-Driven AI for Mental Health
Research type
Research Study
Full title
Understanding Fairness in Data-Driven AI for Mental Health
IRAS ID
321072
Contact name
Jonathan Foster
Contact email
Sponsor organisation
University of Sheffield
Duration of Study in the UK
0 years, 6 months, 0 days
Research summary
The overall goal of the Understanding Fairness in Data-Driven AI for Mental Health study will be to understand the fairness issues relevant to the introduction of data-driven AI into mental healthcare. The study’s research objectives will be a) To collect and analyse qualitative data to understand stakeholders’ (service users, clinicians, information officers) concerns with the fair design development and consequences of introducing data-driven AI into mental healthcare b) To collect and analyse quantitative data and to consider the extent to which a comparison of the qualitative/quantitative results confirms and/or enhances understanding of information officers’ concerns about the fair design development and consequences of introducing data-driven AI into mental healthcare c) To develop enhanced fairness criteria for evaluating the design development and consequences of introducing data-driven AI into mental healthcare. The study and its objectives will be principally accomplished via the conduct of qualitative interviews with service users, clinicians, and information officers; a quantitative survey of information officers with responsibility for handling mental health data, and the development of enhanced fairness criteria informed by an understanding and merging of the qualitative and quantitative results. Finally, the findings of the study will be discussed at a workshop session with service users, clinicians, and information officers.
REC name
South West - Central Bristol Research Ethics Committee
REC reference
22/SW/0153
Date of REC Opinion
8 Nov 2022
REC opinion
Unfavourable Opinion