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Updated: Oct 1, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Explanatory pragmatism: a context-sensitive framework for explainable medical AI.

Rune Nyrup^1,2, Diana Robinson^1,3,4

¹Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.

Ethics and Information Technology

|March 7, 2022

Summary

This summary is machine-generated.

Explainable AI (XAI) needs a unified definition. This study introduces Explanatory Pragmatism, a framework to define explainability based on context, audience, and purpose, reducing miscommunication in AI research.

Keywords:

Ethics of artificial intelligence Explainable artificial intelligence Explanation Medical artificial intelligence Understanding XAI

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Area of Science:

Artificial Intelligence
Human-Computer Interaction
Philosophy of Technology

Background:

Explainable AI (XAI) is a growing field focused on making AI systems understandable.
Explainability is recognized as context-, audience-, and purpose-dependent.
A lack of a unified definition risks miscommunication among multidisciplinary researchers.

Purpose of the Study:

To address the problem of miscommunication in XAI due to varying definitions of explainability.
To propose a unified framework for conceptualizing explainability.
To identify and clarify normative disagreements in XAI research.

Main Methods:

Introduction of the Explanatory Pragmatism framework.
Conceptual analysis of explainability as a context-, audience-, and purpose-relative phenomenon.
Application of the framework to a case study on predicting recovery of consciousness in patients.

Main Results:

The Explanatory Pragmatism framework offers a unified definition of explainability.
It allows for conceptualizing explainability in context-, audience-, and purpose-relative terms.
The framework highlights normative disagreements and distinguishes dimensions of AI explainability.

Conclusions:

Explanatory Pragmatism provides a robust framework for understanding and discussing AI explainability.
It facilitates clearer communication and resolves conflicting claims about explainability.
The framework is applicable to real-world AI applications, such as medical prognostics.