  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.
