Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations

Researchers establish connections between Consistency-Based Diagnosis (CBD) and Actual Causality frameworks within Explainable AI (XAI), addressing a gap in how diagnosis systems explain their outputs. This theoretical work bridges two previously disconnected areas in AI research, with potential applications for making data management systems more interpretable and trustworthy.

This academic paper addresses a fundamental challenge in artificial intelligence: making complex diagnostic systems explainable to users and stakeholders. Consistency-Based Diagnosis, a diagnostic methodology that has existed in computer science for decades, has remained largely outside the XAI discourse despite its relevance to interpretability. The authors recognize that CBD's approach to identifying root causes in faulty systems can be meaningfully connected to frameworks of actual causality and causal responsibility—concepts that have gained prominence in recent XAI research.

The significance of this work lies in its potential to unify two distinct research traditions. CBD systems determine which components are responsible for failures by checking consistency with observed behavior, while actual causality frameworks provide formal definitions of what constitutes a genuine cause versus mere correlation. By reconciling these approaches, researchers can develop more robust explanations for why diagnostic systems reach their conclusions.

For practitioners in data management and AI systems, this connection offers practical value. Current XAI methods often struggle with complex systems where multiple factors contribute to outcomes. A unified framework combining CBD's diagnostic rigor with causality theory's explanatory power could enhance transparency in critical applications like healthcare diagnostics, financial systems, and infrastructure monitoring. The implications extend beyond theoretical computer science into sectors where regulatory compliance and user trust depend on understandable AI decisions.

The research points toward future development of diagnostic systems that not only identify problems but also provide stakeholders with scientifically grounded explanations. This could accelerate adoption of AI in domains where explainability requirements currently limit deployment.

• →Consistency-Based Diagnosis receives minimal attention in XAI research despite its diagnostic capabilities and relevance to explainability
• →Connecting CBD with actual causality frameworks enables more rigorous, scientifically-grounded explanations from diagnostic systems
• →This theoretical bridge has practical applications for data management systems requiring transparency and user trust
• →The work addresses a gap in how complex systems can communicate their reasoning to stakeholders and regulators
• →Future diagnostic AI systems could leverage this framework to provide more trustworthy and interpretable outputs