Representational Ethical Model Calibration

TL;DR

This paper introduces a framework for quantifying and ensuring epistemic equity in healthcare decision models by evaluating model fidelity over diverse, learned representations of patient identity, aiming to improve fairness and reliability.

Contribution

It presents a novel framework called Representational Ethical Model Calibration that quantifies and calibrates model fairness using multi-dimensional identity representations.

Findings

Demonstrated the framework on UK Biobank data

Quantified model performance across diverse representations

Enabled responsive remediation for fairness improvements

Abstract

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.