Understanding Fairness in Data-Driven AI for Mental Health

研究问题研究的目的将是了解与在精神健康中引入数据驱动的人工智能相关的伦理问题，并为旨在缓解NHS精神健康服务中的不平等的人工智能应用程序的设计制定公平措施。该研究的研究目标将是。了解利益相关者对将数据驱动的人工智能应用于心理健康的设计开发和后果的伦理关切。B.考虑利益相关者的道德关切在多大程度上可推广到心理健康服务提供者和使用者群体。C.为设计旨在缓解NHS精神健康服务不平等的人工智能应用程序开发增强的公平措施。背景以不公正、公平和发展公平措施为指导目标，该研究将通过进行混合方法研究来增强关于精神健康护理中数据驱动的人工智能的现有知识，以在相关参与者(即少数族裔服务用户、信息官员、临床医生)的生活经验和视角中建立什么是公平的数据驱动的人工智能的想法。这将主要通过以下方式完成：i)对少数族裔背景的精神健康服务使用者、信息官员和临床医生进行定性访谈；ii)根据定性结果设计和实施定量调查，考虑结果对更广泛的精神保健服务提供者和使用者群体的普适性；iii)根据定性和定量结果制定公平标准和衡量标准。方法该项目将通过定性和定量研究相结合的方式进行。其目的将是利用定性访谈数据和定量调查数据来制定测量方法，以测试数据驱动的人工智能模型在精神卫生决策中的公正性。PUBLIC参与度和IMPACT我们进行这项研究的主要动机是探索在发展数据驱动的人工智能以支持精神卫生领域的医疗决策时公平的伦理作用。这需要制定公平措施和以相关利益攸关方的观点和经验为基础的用例，包括来自少数民族背景的服务用户、信息专业人员和临床医生。鉴于这种动机和承诺，我们将寻求参与对话，探索每个利益相关者群体对采用数据驱动的人工智能的数据、算法和医生-患者决策方面和后果的偏见。以及公平的作用和其他与公平有关的伦理方面，例如平等参与、患者有平等的机会贡献知识。通过审议讲习班，我们将努力促进这些不同观点的谈判和平衡。

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.