In Defense of Post Hoc Explanations in Medical AI

TL;DR

This paper argues that post hoc explanations in medical AI can still be useful even if they don't fully replicate the AI's reasoning.

Contribution

The paper defends the practical value of post hoc explanations in improving clinician-AI collaboration and decision justification.

Findings

Post hoc explanations can enhance functional understanding of black box AI systems.

They can increase the accuracy of clinician-AI teams.

They help clinicians justify AI-informed decisions.

Abstract

Since the early days of the explainable artificial intelligence movement, post hoc explanations have been praised for their potential to improve user understanding, promote trust, and reduce patient‐safety risks in black box medical AI systems. Recently, however, critics have argued that the benefits of post hoc explanations are greatly exaggerated since they merely approximate, rather than replicate, the actual reasoning processes that black box systems take to arrive at their outputs. In this paper, we aim to defend the value of post hoc explanations against this recent critique. We argue that even if post hoc explanations do not replicate the exact reasoning processes of black box systems, they can still improve users’ functional understanding of black box systems, increase the accuracy of clinician‐AI teams, and assist clinicians in justifying their AI‐informed decisions. While post hoc explanations are not a silver‐bullet solution to the black box problem in medical AI, they remain a useful strategy for addressing it.