Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy

Technical Summary: Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy

Problem Statement
Medical Artificial Intelligence (AI) is poised to transform clinical practice, yet the opacity of many Machine Learning (ML) models ("black boxes") poses significant challenges in high-stakes medical contexts where decisions impact life and death. While Explainable AI (XAI) has been proposed as a remedy to clarify model predictions, the field suffers from a lack of conceptual clarity regarding what constitutes an "adequate" medical explanation. Current XAI research often operates without a rigorous philosophical foundation, leading to debates on whether explanations should prioritize scientific causal standards or performance-oriented criteria. Furthermore, existing XAI frameworks frequently treat explanations as technical artifacts evaluated by metrics or user plausibility, potentially overlooking the epistemic and relational requirements essential for clinical trust and decision-making.

Methodology
This paper employs a critical review methodology situated at the intersection of the philosophy of science and XAI. Rather than proposing new algorithms or conducting empirical experiments, the authors synthesize established philosophical literature on medical explanation with current developments in medical AI. The analysis focuses on three core thematic axes derived from philosophical inquiry:

1. Causality: Examining the tension between correlation and causation, and the necessity of different causal models (mechanistic, probabilistic, relational) in medical reasoning.
2. Trust: Analyzing the epistemic and relational dimensions of trust in clinical settings, distinguishing between reliance, specific trust, and general trust within the fiduciary doctor-patient relationship.
3. Explanatory Adequacy: Defining the criteria for "bona fide" medical explanations, considering the pragmatic needs of diverse stakeholders (clinicians, patients, regulators).

Key Contributions
The paper makes three distinct contributions to the discourse on Medical XAI:

1. Critical Gap Analysis: It identifies the conceptual asymmetry between the expectations set by philosophical accounts of explanation and the current capabilities of AI explanations in healthcare. It argues that XAI research has largely overlooked the foundational philosophical literature necessary for grounding explainability.
2. Identification of Core Analytical Axes: The paper delineates three central areas drawn from philosophical literature that are critical for XAI:
   • Causality: Moving beyond "billiard-ball" mechanistic models to embrace a "web of causation" and probabilistic causality that reflects the complexity of diseases.
   • Trust: Framing trust not merely as user satisfaction or accuracy, but as a sui generis concept rooted in fiduciary obligation and vulnerability, requiring justification beyond mere performance.
   • Adequacy: Establishing that a "good" explanation is context-dependent, serving different functions (prediction, intervention, narrative) for different stakeholders.
3. Philosophically Grounded Design Principles: The authors propose a set of design principles (summarized in Table 1) to guide the development of Medical XAI systems:
   • P1: Explanations must support intervention. Explanations should track genuine interventionist dependencies rather than mere associations, enabling clinicians to determine actionable changes.
   • P2: Explanations are stakeholder-specific. XAI frameworks must be adaptive, presenting causal, statistical, or narrative content tailored to the specific epistemic needs of clinicians, patients, or regulators.
   • P3: Trust must be explicitly assessed. Evaluation protocols must distinguish between plausibility and faithfulness, ensuring explanations reinforce justified reliance rather than misplaced confidence.

Results and Illustrative Analysis
The paper does not present experimental results but offers a conceptual framework for evaluating existing XAI methods. Using Figure 1, the authors illustrate how this framework can assess the "epistemic commitment" of different techniques:

• Saliency Maps (e.g., Grad-CAM++): The analysis suggests these methods often score low on causal intervention and stakeholder specificity. While they may appear effective, they provide correlational information that fails to offer causal insight or support the fiduciary trust required in clinical practice. They risk increasing user confidence without improving decision quality.
• Counterfactual/Generative Methods (e.g., GANterfactual): In contrast, methods that generate counterfactual scenarios (e.g., showing how a chest X-ray must change to alter a diagnosis) are positioned higher on the framework. They provide explicit causal insights, support interventionist reasoning, and can be tailored to different stakeholders, thereby better satisfying the criteria for epistemic adequacy.

Significance and Claims
The paper claims that for XAI to become a legitimate component of clinical reasoning and accountable medical practice, it must move beyond instrumental views of explanation. The significance of this work lies in:

• Reframing the Debate: It shifts the discussion from a trade-off between accuracy and interpretability to a requirement for explanations that are epistemically robust and aligned with the practical needs of clinical decision-making.
• Bridging Disciplines: It integrates the rich philosophical traditions of medical explanation (often overlooked in AI research) into contemporary XAI debates, arguing that medicine has long dealt with complex questions of causation and trust that XAI must address.
• Establishing Criteria: It provides a necessary conceptual foundation for assessing whether XAI approaches meet the epistemic, justificatory, and practical requirements of the health sciences.

The authors conclude modestly that while this work provides a conceptual and philosophical framework, it does not systematically evaluate all existing methods against these criteria. They identify this as a limitation to be addressed in future work through a systematic analysis of how current medical XAI approaches incorporate these philosophical dimensions.