Explainable AI (XAI) Analyses

• Explainable Artificial Intelligence (XAI) analyses are multi-dimensional frameworks that provide clear, structured, and interactive explanations tailored to different user needs.
• The framework categorizes systems into five graded levels, from no explanation to dynamic, user-adaptive dialogue, ensuring both technical and social validity.
• This blueprint for XAI research integrates historical, causal, and abductive reasoning methodologies to calibrate AI systems for enhanced transparency and trust.

Explainable Artificial Intelligence (XAI) analyses encompass a multi-dimensional field concerned with the design, assessment, and theoretical grounding of AI systems capable of providing justifiable, interpretable, and user-aligned explanations for their decisions. This domain underpins essential aspects such as trust, regulatory compliance, and social acceptance, particularly as AI permeates high-stakes contexts across industry and society. The following sections detail the foundational requirements, conceptual lineage, staged maturity model, research implications, and methodological blueprint for XAI as synthesized from contemporary research.

1. Foundational Requirements for XAI Systems

Contemporary XAI frameworks delineate a “strategic inventory” of components that must be integrated to achieve genuine and trusted explainability (Atakishiyev et al., 2020). These requirements are:

• Explicit-Explanation Representation: The system must provide clear, structured explanations suitable for direct interrogation and reporting.
• Alternative Explanations: The system should offer multiple plausible explanations tailored to diverse user perspectives or information needs.
• Knowledge of the Explainee: It is essential to consider or directly model the background knowledge, expectations, and goals of the explanation recipient; explanations must be adaptive to user expertise and context.
• Interactivity: Robust XAI systems must allow users to engage in iterative dialogue, enabling follow-up or “what-if/why-not” queries so that explanations can be incrementally refined or challenged.

The roles of these facets are not reducible to a sum of parts; each aligns with distinct traditions in the theory and practice of scientific explanation, underpinning system robustness and the potential for social legitimacy.

2. Historical and Conceptual Foundations

The development of XAI is situated within a broader intellectual lineage spanning abductive reasoning, scientific explanation models, causal inference, and formal logical tradition (Atakishiyev et al., 2020):

• Abductive Reasoning: Initiated by C. S. Peirce, abduction refers to the generation of the best explanatory hypotheses accounting for given observations—a tradition extended in early AI systems and inductive logic programming.
• Deductive-Nomological and Scientific Models: Classical models emphasize that an explanation must reveal not just that an output holds (the “what”) but why, embedding the event coherently within general laws or principles.
• Causal Modelling: Modern causal models, notably formalized by Pearl, stress that explanations must uncover the underlying mechanisms and not merely surface correlations.
• Mechanism versus Semantics: The framework distinguishes internal, mechanistic explanations (computation or debugging traces) from outward-facing, semantic explanations that end-users can understand—a reflection of the divide between formal syntactic transparency and context-driven meaningfulness.

This historical dialogue compels XAI research to interface statistical, causal, and epistemic traditions in order to enable layered explanations ranging from technical debugging to lay comprehension.

3. Five-Graded Levels of Explanation

A central contribution is the hierarchization of XAI capability into five ordered levels (Atakishiyev et al., 2020), similar in spirit to automation scales in autonomous vehicles. These are:

Attribute	Level 0	Level 1	Level 2	Level 3	Level 4
Explicit Representation	–	✓	✓	✓	✓
Alternative Explanations	–	–	✓	✓	✓
Knowledge of Explainee	–	–	–	✓	✓
Interactivity	–	–	–	–	✓

• Level 0: No explanation—complete “black box.”
• Level 1: Single modality explained (e.g., a heatmap) via post hoc or auxiliary methods.
• Level 2: Multiple modalities (e.g., textual and visual explanations) provided, allowing alternative explanatory routes.
• Level 3: Explanations tailored to the explainee’s knowledge, role, or background (adaptation).
• Level 4: Full interactive, dialogic system supporting multi-turn, user-driven exploration and challenge of the decision logic.

This progression formalizes how explainability can mature from mere annotation to context-sensitive, dynamically evolving knowledge transfer.

4. Implications for Transparent and Trusted AI

The multi-component, staged XAI framework yields the following implications for trustworthy AI (Atakishiyev et al., 2020):

• Theory-Grounded Explanations: Explanations must be anchored within the scientific traditions of abduction, scientific rationality, and causality, eschewing ad hoc or superficial outputs.
• Tailoring and Context Sensitivity: The need for explanations to be adapted to the user context is foregrounded, acknowledging the spectrum from lay to expert.
• Iterative, Evolvable Dialogue: Trust and transparency are enhanced as explanations shift from static overlays to interactive, responsive processes.
• Technical and Social Legitimacy: The explanatory property of an AI system must address both the technical (debugging, auditing) and the social (acceptability, accountability) requirements, becoming a system property rather than a documentation supplement.

5. Blueprint for XAI Research and Evaluation

The synthesis offers a high-level model for the ongoing empirical and conceptual assessment of XAI systems:

1. Inventory and Calibration: Classify systems according to the inventory (representation, alternatives, user modelling, interactivity) and assign appropriate level per the five-stage model.
2. Historical Anchoring: Map existing explanation mechanisms against the theoretical traditions (abduction, DN, causality, logic) to ensure coverage of explanatory desiderata.
3. User-Centric Validation: Develop empirical protocols to determine whether the system explanation satisfies its intended role for its actual users (e.g., via mental model alignment, task performance augmentation, or iterative knowledge transfer).
4. Iterative Design Paradigm: Integrate continuous feedback and redesign, especially for interactive and adaptive explanation regimes, as alignment with user needs cannot be guaranteed a priori.

6. Contextualization and Limitations

The current state of XAI research, as reflected in the proposed framework, emphasizes that most extant systems remain at Levels 1–2, with few instances achieving user-adaptive (Level 3) or truly interactive (Level 4) operation (Atakishiyev et al., 2020). The integration of alternative explanations, adaptive tailoring, and interactivity represents an open frontier, where methodological and computational obstacles—such as robust user modelling and the design of explanation languages for dialogue—must be addressed for advancement.

7. Summary

The “multi-component framework” for XAI analyses synthesizes a strategic inventory of requirements and a stepwise model of explanatory maturity, rooted in historical theory and practical needs. This dual-perspective approach both clarifies what makes an XAI system adequately transparent and trustworthy and provides a structured blueprint for empirical research and system development. The result is a multi-layered taxonomy and roadmap, enabling rigorous classification, analysis, and iterative advancement of explainable AI architectures suitable for varied stakeholders and applications.