Equity is widely held to be fundamental to the ethics of healthcare. In the context of clinical decision-making, it rests on the comparative fidelity of the intelligence -- evidence-based or intuitive -- guiding the management of each individual patient. Though brought to recent attention by the individuating power of contemporary machine learning, such epistemic equity arises in the context of any decision guidance, whether traditional or innovative. Yet no general framework for its quantification, let alone assurance, currently exists. Here we formulate epistemic equity in terms of model fidelity evaluated over learnt multi-dimensional representations of identity crafted to maximise the captured diversity of the population, introducing a comprehensive framework for Representational Ethical Model Calibration. We demonstrate use of the framework on large-scale multimodal data from UK Biobank to derive diverse representations of the population, quantify model performance, and institute responsive remediation. We offer our approach as a principled solution to quantifying and assuring epistemic equity in healthcare, with applications across the research, clinical, and regulatory domains.
翻译:在临床决策方面,它依赖情报 -- -- 以证据为基础或直觉为基础 -- -- 的相对真实性来指导每个病人的管理。虽然当代机器学习的内在力量最近引起注意,但这种认知性公平在任何决定指导(无论是传统还是创新)中产生。然而,目前还没有关于其量化的一般框架,更不用说保证了。我们在这里,在根据为最大限度地实现人口多样性而设计的知识性多维特征表现来评价模型性忠诚性方面,形成了典型的公平性,引入了代表道德模式统一的全面框架。我们展示了英国生物银行大规模多式数据框架用于对人口进行多样化的表述、量化模型性能和进行响应性补救。我们提出一种原则性解决办法,用以量化和确保保健方面的认知性公平性,并在整个研究、临床和监管领域应用。