Responsibility Gaps and Black Box Healthcare AI: Shared Responsibilization as a Solution

Area of Science:

• Medical Ethics
• Artificial Intelligence
• Healthcare Administration

Background:

• Increasing integration of sophisticated artificial intelligence (AI) into healthcare raises questions about accountability for AI actions.
• Responsibility gaps emerge when AI errors occur in automated tasks, leaving a void in moral blame attribution.

Purpose of the Study:

• To examine the generation of responsibility gaps by "black box" AI in healthcare.
• To address the moral implications of unaddressed responsibility gaps.
• To propose a solution for the ethical integration of AI in healthcare.

Main Methods:

• Philosophical analysis of AI-induced responsibility gaps.
• Ethical examination of accountability in healthcare AI.
• Proposal of stakeholder "responsibilization" as a solution.

Main Results:

• "Black box" healthcare AI demonstrably generates responsibility gaps.
• Unaddressed gaps pose significant moral challenges in healthcare.
• Voluntary assumption of responsibility by stakeholders is a viable solution.

Conclusions:

• Stakeholders can "responsibilize" AI gaps, assuming moral responsibility.
• This approach permits the use of "black box" healthcare AI.
• Ethical frameworks must adapt to AI integration in critical sectors like healthcare.