Understanding Fairness in Data-Driven AI for Mental Health

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RESEARCH PROBLEMThe aims of the research study will be to understand the ethical issues relevant to the introduction of data-driven AI in mental health and to develop fairness measures for the design of an AI application aimed at mitigating inequalities in NHS mental health services. The study's research objectives will bea. To understand stakeholders' ethical concerns about the design development and consequences of applying data-driven AI in mental health. b. To consider the extent to which the stakeholders' ethical concerns are generalizable to the population of mental health service providers and users. c. To develop enhanced fairness measures for the design of an AI application aimed at mitigating inequalities in NHS mental health services.BACKGROUNDTaking injustice, fairness and the development of fairness measures as its guiding goals, the study will enhance existing knowledge about data-driven AI in mental health care by conducting a mixed-methods study that grounds ideas of what is fair data-driven AI in the lived experiences and perspectives of its relevant participants, i.e. ethnic minority service users, information officers, clinicians. This will be principally accomplished via i) the conduct of qualitative interviews with users of mental health services from ethnic minority backgrounds, with information officers, and with clinicians ii) The design and implementation of a quantitative survey, informed by the qualitative findings, that considers the generalizability of the results to a broader population of service providers and users in mental healthcare iii) The development of fairness criteria and measures on the basis of the results of the qualitative and quantitative results. METHODSThe project will be conducted via a combination of qualitative and quantitative research. The intent will be to draw on the qualitative interview data and the quantitative survey data to develop measures for testing the fairness of data-driven AI models for informing decision-making in mental healthcare.PUBLIC ENGAGEMENT AND IMPACTOur key motivation for undertaking the research is to explore the ethical role of fairness when developing data-driven AI to support medical decision-making in mental healthcare. This entails the development of fairness measures and a use case that is anchored in the perspectives and experiences of relevant stakeholders, including services users from ethnic minority backgrounds, information professionals, and clinicians. Given this motivation and commitment, we will be seeking to engage in conversations that explore the prejudices of each of these stakeholder groups towards the data, algorithmic, and doctor-patient decision-making aspects and consequences of adopting data-driven AI. As well as the role of fairness and other ethical aspects related to fairness e.g. equal participation, equal opportunity for patients to contribute knowledge. Via Deliberative Workshops we will seek to facilitate the negotiation and balancing of these different perspectives.