Multi-Level Explainability Pipeline

• Multi-level explainability pipelines are systematic frameworks that decompose AI reasoning into hierarchically organized, verifiable stages to enhance transparency and trust.
• They integrate iterative verification and user feedback to tailor explanations for diverse stakeholder needs across critical domains.
• They deliver measurable improvements in error reduction and attribution fidelity, validated through metrics like reduced error rates and increased transparency scores.

A multi-level explainability pipeline refers to a systematic, modular framework that decomposes model reasoning or explanation processes into sequential, hierarchically organized stages or layers, with the aim of improving transparency, correctness, modularity, user adaptation, and societal trust of AI systems. Such pipelines are prevalent in state-of-the-art explainable AI (XAI) and are instantiated in diverse domains—from natural language processing and reinforcement learning to scientific imaging and high-stakes decision support—where intermediate outputs are interpreted, checked, and aggregated at multiple granularity levels.

1. Foundational Principles and Motivations

Multi-level explainability pipelines are motivated by the limitations of single-stage, post hoc, or monolithic explanation techniques. Key deficiencies addressed include:

• Opaque Intermediate Reasoning: Conventional methods such as standard chain-of-thought (CoT) yielding only an unverified linear rationalization, without checkpointing or error correction of intermediate steps (Sanwal, 29 Jan 2025).
• Limited Semantic Coverage: Feature attributions (e.g., LIME, SHAP) lack the ability to explain predictions at the level of user-relevant concepts or higher-level causes (Poché et al., 10 Dec 2025, Kůr et al., 4 Nov 2025).
• Insufficient User Adaptability: Explanations often fail to match the epistemic needs or cognitive styles of diverse stakeholders, from domain experts to lay users (Bello et al., 6 Jun 2025, Atakishiyev et al., 2020).
• Inadequate Trust and Auditability: In critical applications, explainability pipelines must produce artifacts for auditing, regulatory review, and social trust (Pehlke et al., 10 Nov 2025).

Consequently, modern approaches architect multi-level pipelines to segment and verify reasoning, incorporate side information or domain knowledge, aggregate explanations across levels, and tailor outputs to specific stakeholder groups.

2. Canonical Pipeline Architectures

Distinct instantiations of the multi-level explainability paradigm exist across research domains, but most share structural commonalities. The following table summarizes representative pipelines:

Publication	Levels / Stages	Key Mechanisms
(Sanwal, 29 Jan 2025)	Layered-CoT: Reasoning → Verification → (User Feedback)	LLM/agent layers, external checks, feedback
(Poché et al., 10 Dec 2025)	Attribution → Concepts → Aggregation/Dashboard	Attributions, unsupervised concepts, reporting
(Kůr et al., 4 Nov 2025)	CRP → Prototype Extraction → LLM Naming → Explanation	Relevance maps, LLM labeling, modular output
(Ramamurthy et al., 2020)	Local → Clustered/Group → Global (Tree)	Fusion-penalty clustering, ADMM solver
(Bello et al., 6 Jun 2025)	Algorithmic (φ/R/P/C) → Interactive → Social (LLM)	User interface, adaptation, LLM summarization
(Condrea et al., 2024)	Classifier → Explainability (IG) → Pseudo-mask → Segmentation	IG-based refinement, clustering, multi-stage
(Pehlke et al., 10 Nov 2025)	Sensitivity model → Game-theoretic (Normal/Seq.)	Modular agent/analyzer, checked intermediates

These designs segment the explanation process according to sources of evidence (e.g., feature, concept, rule, counterfactual), levels of abstraction, stakeholder requirements, or auditing demands.

3. Formal Components and Layer Definitions

Formally, pipelines embed multi-level decomposition via layer-wise modules and interfaces. Taking Layered Chain-of-Thought (Layered-CoT) as a paradigmatic example (Sanwal, 29 Jan 2025):

• For an overall task $Q$, reasoning is split into $L$ ordered layers $\ell=1\ldots L$.
• Reasoning Module $R_\ell$: $R_\ell : X_\ell \to Y_\ell$ processes the input context $X_\ell = \{ Q, Y_1, \ldots, Y_{\ell-1} \}$ and outputs a partial explanation $Y_\ell$.
• Verification Function $V_\ell$: $V_\ell : Y_\ell \to [0,1]$ assigns a confidence/consistency score, typically by cross-checking $Y_\ell$ via external resources or contradiction detection. The output is accepted only if $V_\ell(Y_\ell) \geq \tau_\ell$.
• Optional User Feedback $F_\ell$: If enabled, $F_\ell(Y_\ell) \to \delta_\ell$ incorporates corrections, which update $X_\ell$ and trigger re-invocation as needed.
• The iteration continues until acceptance, and outputs are concatenated: $E_{final} = Y_1 \|\ldots\| Y_L$.

Similar layering is found in model-agnostic multilevel trees (Ramamurthy et al., 2020), where the fusion parameter $\beta$ creates a hierarchy from local surrogates (leaves) through group-level (internal) to global (root) explanations. In concept-based pipelines such as LLEXICORP (Kůr et al., 4 Nov 2025), layers include: channel relevance, prototype selection, concept naming, grounded explanation, and level-of-detail aggregation for expert/audience targeting.

4. Multi-Level Customization, User Adaptation, and Stakeholder Alignment

A defining trait of multi-level pipelines is their capacity for tailored outputs:

• Stakeholder Alignment: Explicit support for technical, domain expert, and societal end-users in layered frameworks; Level 1 for technical fidelity (feature attributions, rule paths, etc.), Level 2 for human-centered/interactive refinement (user feedback, scenario queries), Level 3 for socially accessible, LLM-mediated narratives (Bello et al., 6 Jun 2025).
• Dynamic User Engagement: Integration of interactive loops for feedback, correction, and scenario simulation, increasing engagement only when domain complexity necessitates it (Sanwal, 29 Jan 2025, Atakishiyev et al., 2020).
• Customization of Output Detail: As in LLEXICORP, audience parameterization switches between expert-level detail (layer/channel references, technical terms, exact percentages) and plain-language, non-technical summaries (Kůr et al., 4 Nov 2025).

These properties substantiate the ethical, regulatory, and adoption rationales for explainability in critical and regulated domains.

5. Quantitative Performance, Validation, and Fidelity

Quantitative assessment is foregrounded at each level in high-impact pipelines:

• Layered-CoT (Sanwal, 29 Jan 2025): Demonstrates $\sim30\%$ reduction in error rate and doubled transparency score over vanilla CoT at fixed explanation quality, with user engagement adaptively increasing for high-complexity domains.
• Model-Agnostic Multilevel Explanations (Ramamurthy et al., 2020): Empirically achieves higher fidelity and cluster-level interpretability versus alternatives (e.g., $R^2$, Kendall’s $\tau$ for feature-importance rank alignment).
• LLEXICORP (Kůr et al., 4 Nov 2025): Human study shows $78\%$ pattern agreement, $83\%$ summary faithfulness, and up to $92\%$ usefulness when pattern+localization concur; segmentation approaches (Condrea et al., 2024) achieve F1 scores competitive with fully supervised benchmarks, validating the pipeline's efficacy in label-scarce settings.

Evaluation is multidimensional, encompassing error reduction, transparency, faithfulness, user engagement, and scalability metrics.

6. Applications and Domain-Specific Variants

Multi-level explainability pipelines have been deployed in:

• Medical diagnostics: Iterative slice classification, explainability-based pseudo-label generation, and segmentation for pulmonary embolism imaging (Condrea et al., 2024).
• Financial and risk analysis: Stepwise scrutiny of fundamentals, risk factors, and market landscape, with agent-based verification at each stage (Sanwal, 29 Jan 2025).
• Scientific and spatiotemporal forecasting: Cluster-segregate-perturbation paradigms for regional weather effect analysis, supporting both global and localized insight (Verma et al., 2024).
• Reinforcement learning: Two-level vision–decision pipelines in RL with transparent object-centric perception followed by symbolic reasoning (Custode et al., 2022).
• LLM-based explainers and dashboards: Layered attribution–concept–aggregation pipelines, merging token/feature-level saliency with unsupervised concept extraction and importance scoring (Poché et al., 10 Dec 2025).

Such diversity demonstrates the universality of the layered paradigm and its adaptability to model class, data type, and domain requirements.

7. Limitations, Open Challenges, and Future Directions

Despite measurable gains, current pipelines face recognized open challenges:

• Increased Latency and Cost: The chaining of multiple layers, external resource queries, and iterative verification lead to added computational and operational expense (Sanwal, 29 Jan 2025).
• Dependency on Domain-Specific Artifacts: Reliance on structured APIs, knowledge graphs, and thresholds requires careful tuning and maintenance (Sanwal, 29 Jan 2025, Pehlke et al., 10 Nov 2025).
• Verifiability and Faithfulness: No formal guarantee of minimality or faithfulness in LLM-based causal explanation; hallucinations and stability remain problematic for unsupervised concept pipelines (Bhattacharjee et al., 2023, Kůr et al., 4 Nov 2025).
• Evaluative Standardization: The absence of universal, multi-level metrics for fidelity, utility, and societal acceptability; future research is called to develop multi-axis benchmarks and participatory auditing protocols (Bello et al., 6 Jun 2025).
• Scalability and Automation: Efforts are ongoing toward partially automating verification with web or KG wrappers, adaptive depth selection, and modal extensibility to new domains (Sanwal, 29 Jan 2025, Verma et al., 2024).

The constructive trajectory involves deeper integration of automated checks, adaptivity to data/task complexity, broader support for interactive and societal layers, and principled validation frameworks.

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In summary, multi-level explainability pipelines constitute a robust, empirically validated paradigm for XAI that decomposes model reasoning or attributions into hierarchically verifiable, user-adaptable stages across diverse problem domains. By advancing beyond single-form or monolithic interpretations, these pipelines provide modular, auditable, and stakeholder-aligned explanations that underpin trust, regulatory compliance, and meaningful human-AI collaboration in high-stakes environments (Sanwal, 29 Jan 2025, Poché et al., 10 Dec 2025, Bello et al., 6 Jun 2025, Pehlke et al., 10 Nov 2025, Ramamurthy et al., 2020, Condrea et al., 2024).