Representational ethical model calibration.

Area of Science:

• Healthcare Ethics
• Machine Learning
• Biomedical Informatics

Background:

• Equity is a cornerstone of healthcare ethics, particularly in clinical decision-making.
• Assessing the fairness of decision-making tools, including AI, is crucial for equitable patient outcomes.
• Existing methods lack a general framework for quantifying epistemic equity in healthcare.

Purpose of the Study:

• To formulate and introduce a comprehensive framework for Representational Ethical Model Calibration (REMC).
• To quantify and assure epistemic equity in healthcare decision-making processes.
• To address the challenge of evaluating the comparative fidelity of intelligence guiding patient management.

Main Methods:

• Epistemic equity was formulated using model fidelity over multidimensional identity representations.
• Representations were crafted to maximize captured population diversity.
• The framework was demonstrated on large-scale multimodal data from UK Biobank.

Main Results:

• Diverse population representations were derived from UK Biobank data.
• Model performance was quantified, enabling the identification of disparities.
• Responsive remediation strategies were instituted based on performance evaluation.

Conclusions:

• The proposed REMC framework offers a principled solution for quantifying and assuring epistemic equity.
• This approach has broad applications across research, clinical practice, and regulatory oversight in healthcare.
• Ensuring epistemic equity is vital for ethical and effective AI-driven healthcare solutions.