Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning

Area of Science:

• Public Policy
• Health Economics
• Bioethics

Background:

• Distributing scarce medical resources involves complex trade-offs between efficiency and fairness.
• Diverse stakeholder values complicate policy development in healthcare allocation.

Purpose of the Study:

• To present a general framework for resource allocation policy development.
• To illustrate this framework using organ transplant allocation.
• To develop an analytical tool for exploring policy outcomes.

Main Methods:

• Combining ethical theory, data modeling, and stakeholder input.
• Developing a novel analytical tool using machine learning and optimization.
• Case study application to organ transplant allocation policies.

Main Results:

• The framework facilitates balancing multiple objectives in policy design.
• The analytical tool enables efficient exploration of policy outcomes.
• Stakeholders can develop evidence-based value judgments on tradeoffs.

Conclusions:

• The proposed framework and tool enhance policymaking for scarce resource distribution.
• Improved stakeholder engagement leads to more informed and equitable allocation policies.
• This approach supports evidence-based decision-making in critical healthcare policy areas.