In Defense of Post Hoc Explanations in Medical AI

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Human-Computer Interaction

Background:

• Post hoc explanations in explainable artificial intelligence (AI) aim to improve user trust and safety in medical AI.
• Recent critiques question the utility of post hoc explanations, stating they approximate rather than replicate AI reasoning.
• This debate is crucial for the responsible development and deployment of AI in healthcare.

Purpose of the Study:

• To defend the value of post hoc explanations against recent criticisms.
• To demonstrate that post hoc explanations offer significant benefits despite not perfectly mirroring AI processes.
• To highlight the practical utility of post hoc explanations in clinical settings.

Main Methods:

• The study presents a conceptual defense of post hoc explanations.
• It analyzes the functional benefits of these explanations in the context of medical AI.
• The authors review existing arguments and propose a counter-argument based on practical utility.

Main Results:

• Post hoc explanations can significantly improve users' functional understanding of black box AI systems.
• They enhance the accuracy and effectiveness of clinician-AI teams.
• These explanations aid clinicians in justifying decisions informed by AI outputs.

Conclusions:

• Post hoc explanations are a valuable strategy for addressing the limitations of black box medical AI.
• While not a complete solution, they offer tangible benefits for understanding, trust, and decision-making.
• Continued research and application of post hoc explanations are warranted in medical AI.