"Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations.

Area of Science:

• Medical Ethics
• Artificial Intelligence in Healthcare
• Distributive Justice

Background:

• Growing use of AI in healthcare presents ethical challenges.
• Advanced machine learning models often achieve high accuracy but lack transparency.
• Debate exists on prioritizing accuracy over explainability in medical AI.

Purpose of the Study:

• To analyze the trade-off between AI accuracy and explainability within distributive justice.
• To examine how algorithmic opacity impacts procedural fairness in healthcare.
• To propose ethical guidelines for deploying unexplainable AI in resource distribution.

Main Methods:

• Conceptual analysis of AI accuracy, explainability, and distributive justice principles.
• Case study of liver transplantation to illustrate fairness issues in resource allocation.
• Ethical argumentation regarding procedural fairness and algorithmic accountability.

Main Results:

• While AI accuracy benefits outcome-oriented justice by maximizing patient benefits and optimizing resources, its lack of explainability undermines procedural fairness.
• Algorithmic opacity threatens accountability, bias avoidance, and transparency in healthcare decision-making.
• Unexplainable AI in critical resource allocation, like liver transplants, leads to procedural unfairness.

Conclusions:

• The opacity of 'black box' AI algorithms poses significant ethical risks to procedural fairness in healthcare.
• Prioritizing AI accuracy without ensuring explainability can lead to unjust distribution of medical resources.
• Ethical recommendations are crucial for the responsible implementation of AI in health resource allocation.