Qi-Framework for XAI

Purpose: To establish a comprehensive, mathematically-grounded framework (Qi-Framework) that categorizes and contextualizes the diverse landscape of explainable AI (XAI) methods. This framework aims to unify explanation approaches through abstract syntax, enabling systematic comparison and evaluation across seemingly disparate XAI techniques. (DOI: 10.21203/rs.3.rs-4824427/v1)

Background: The field of explainable AI has expanded rapidly with numerous methods developed to address the black-box nature of complex models. However, these methods often exist in isolation, with inconsistent terminology and evaluation metrics. The Qi-Framework addresses this fragmentation by proposing a unified mathematical language for describing explanation types, establishing common ground for comparative analysis, and identifying unexplored regions in the XAI landscape.

Techniques & Methodological Contributions: 

  • Explanation Type Taxonomy – Classification of explanation approaches into fundamental categories based on mathematical properties and output characteristics
  • Abstract Syntax Development – Creation of a formal language to describe XAI methods through general mathematical notation that transcends specific implementations
  • Comparative Analysis Framework – Systematic methodology for evaluating explanation methods against consistent criteria rather than domain-specific metrics
  • Historical-Future Relation Mapping – Tracing the evolution of explanation approaches and identifying promising directions for future research
  • Quantitative Benchmarking System – Metrics-based approach for objectively assessing the effectiveness of different explanation techniques across diverse applications

Applications in AI Research & Development: 

This framework serves multiple critical functions within the AI ecosystem:

  • Research Guidance – Identification of underexplored explanation types to direct future XAI research efforts
  • Method Selection – Structured approach for practitioners to select appropriate explanation techniques based on use case requirements
  • Standardized Evaluation – Common metrics and terminology for benchmarking explanation quality across different domains
  • Cross-Disciplinary Integration – Bridging methodologies from computer science, mathematics, cognitive science, and domain expertise
  • Regulatory Compliance – Supporting systematic documentation of AI transparency approaches for emerging AI governance frameworks

Summary of Findings: 

The Qi-Framework successfully demonstrates that diverse XAI approaches can be unified through abstract mathematical syntax, revealing fundamental patterns across seemingly different methods. Through comprehensive literature analysis, the research identifies both well-explored territories and significant gaps in the current XAI landscape. The framework's generative utility enables researchers not only to categorize existing methods but also to derive new approaches by exploring unoccupied regions in the explanation space. This work represents a significant advancement in moving XAI from an ad hoc collection of techniques toward a cohesive, theoretically-grounded discipline with common evaluation standards and developmental trajectories.

Fig. 1: Qi-Framework taxonomy illustrating explanation types and their relationships

Fig. 2: Comparative analysis of existing XAI methods mapped to framework categories

Fig. 3: Historical evolution of explanation approaches shown through framework lens

Fig. 4: Visualization of unexplored explanation territories identified through the framework

Fig. 5: Mathematical formulation showing how diverse XAI methods reduce to common abstract syntax