---
title: 'Holistic-XAI: A Comprehensive Approach'
url: https://www.emergentmind.com/topics/holistic-xai-h-xai
type: topic
---

# Holistic-XAI: A Comprehensive Approach

Holistic-XAI (H-XAI) denotes a family of explainability frameworks that broaden XAI beyond isolated, one-off justifications of model outputs. Across recent formulations, the term refers to approaches that treat explanation as a user-centred, interactive, and often multi-layered process spanning multiple stakeholders, multiple explanation methods, and, in some formulations, the entire machine-learning workflow. This broadening is motivated by the claim that narrow explanations of an individual decision rarely provide insight into an agent’s beliefs and motivations, hypotheses of other agents’ intentions, interpretation of external cultural expectations, or the processes used to generate its own explanation, even though such factors are presented as essential to the explanatory depth required for acceptance and trust [2107.03178].

## 1. Genealogy and definitional scope

The term does not denote a single standardized architecture. Rather, recent literature uses closely related labels—H-XAI, HXAI, and HCXAI—to describe a shift from model-centric explanation toward broader accounts of transparency, interaction, and human alignment. In one precursor, explanation is framed as the outcome of a three-way interplay among a technical model \(M\), the human user \(H\), and the socio-technical context \(C\), summarized as \(E=\mathrm{HCXAI}(M,H,C)\), with explanation selection constrained by fidelity to model behavior but oriented toward trust, understanding, and critical reflection [2002.01092]. A later line explicitly proposes “levels of explanation” and situates “Broad-XAI” as a route toward high-level “strong” explanations rather than only low-level “narrow” ones [2107.03178].

A survey-style formulation presents H-XAI as an end-to-end, multi-layered, multi-disciplinary framework that unifies feature-based and surrogate explanations, concept-level interpretability, human-centric analogy-driven explanations, neuroscience-inspired cognitive modules, affective and personality components, and ethical and consciousness considerations [2402.06673]. Another formulation defines HCXAI as a “three-layered framework that bridges Human-Centered AI (HCAI) and Explainable AI (XAI) to establish a structured explainability paradigm,” combining a foundational AI model with built-in explainability, a human-centered explanation layer, and a dynamic feedback loop [2504.13926].

A concise way to organize the literature is to view Holistic-XAI as an umbrella for several partially overlapping agendas.

| Strand | Main emphasis | Representative paper |
|---|---|---|
| Levels of explanation | From narrow to high-level “strong” explanations | [2107.03178] |
| Reflective sociotechnical HCXAI | Explanation indexed to users, values, and context | [2002.01092] |
| Human-centred evaluation | Explanations as user experience | [2308.06274], [2407.10662] |
| Workflow-wide HXAI | Explanation across data, setup, training, output, quality, and communication | [2508.11529] |
| Multi-method interactive H-XAI | Causal ratings plus post-hoc XAI and baselines | [2508.05792] |
| LLM-mediated H-XAI | Dual-layer expert and lay explanations in one response | [2506.12240] |

This diversity suggests that H-XAI is best understood as a research program rather than a single method. Its common denominator is the rejection of the assumption that explainability is exhausted by a local attribution map or a single counterfactual.

## 2. Core commitments and design principles

A central commitment is the rejection of “single-shot” explanation as the dominant unit of analysis. In the XEQ work, H-XAI “emerges from the recognition that explaining an AI system to stakeholders is not a one-off event but an interactive, ‘multi-shot’ journey in which users explore, probe, and progressively build understanding.” The same source contrasts system-centred metrics such as fidelity or sparsity with explanation experiences centred on personalization, iteration, and the satisfaction of explanation needs over time [2407.10662]. This reorientation is explicitly linked to XAI experiences delivered through chatbots, dashboards, or hybrid GUIs.

A second commitment is stakeholder pluralism. Several H-XAI formulations are explicit that explanations should not primarily serve developers. One 2025 framework states that current XAI methods “largely serve developers” and introduces H-XAI to support “diverse stakeholder needs,” including understanding individual decisions, assessing group-level bias, and evaluating robustness under perturbations [2508.05792]. Another workflow-oriented formulation aligns explanations with “domain experts, data analysts and data scientists” and treats tailoring to user expertise as part of the formal definition of HXAI [2508.11529]. The LLM-based democratization framework extends this further by requiring that one response contain both technical information for experts and a narrative explanation understandable by non-experts [2506.12240].

A third commitment is methodological plurality. Rather than assuming that one explanation technique is sufficient, H-XAI frameworks repeatedly combine multiple methods. The interactive H-XAI framework formalizes this by defining \( \mathrm{H\text{-}XAI}=(S,D,M,B,W) \), where \(M=\{\mathrm{RDE},\mathrm{XAI}\}\) and the XAI set contains SHAP, PDP, and Counterfactuals, while \(B=\{B_{\mathrm{random}},B_{\mathrm{bias}}\}\) provides automatically generated baselines against which causal ratings are compared [2508.05792]. The workflow-wide HXAI taxonomy similarly treats explainability as distributed over six components: Data, Analysis Setup, Learning Process, Model Output, Model Quality, and Communication Channel [2508.11529].

A fourth commitment is that explanation quality is not reducible to fidelity alone. Multiple sources argue that faithfulness, human interpretability, domain trust, and interaction quality are distinct desiderata that can diverge. This point recurs in holistic assessment frameworks and in the psychometric evaluation of XAI experiences [2402.03043; 2407.10662].

## 3. Evaluation frameworks and measurement models

Holistic-XAI has produced several distinct evaluation paradigms. In SIDU-TXT, the evaluation framework is explicitly “holistic” because it combines Functionally-Grounded, Human-Grounded, and Application-Grounded evaluation. Functionally-Grounded evaluation uses proxy tasks or formal metrics without humans in the loop; in that paper, faithfulness is quantified by area under insertion and deletion curves. Human-Grounded evaluation uses lay participants to assess justifiability and comprehensibility, quantified by lexical overlap at the token level and precision, recall, and \(F_1\) on sentence-level selections. Application-Grounded evaluation engages domain experts on a real task and asks which XAI method they trust most and whether legally relevant factors are surfaced [2402.03043].

Donoso-Guzmán et al. propose a broader human-centred evaluation framework for XAI by adapting the User-Centric Evaluation Framework from recommender systems. Their framework contains five components: Objective System Aspects, Explanation Aspects, Subjective System Aspects, User Experience, and Interaction Outcomes. The framework links properties such as fidelity, complexity, cognitive load, trust, usefulness, performance, and reliance, and distinguishes metrics operating at generation, abstraction, format, and communication levels [2308.06274]. This formulation is explicitly holistic in the sense that it treats explanation effects on humans as a complex user experience rather than as a narrow property of the explanatory artifact alone.

The XAI Experience Quality (XEQ) Scale operationalizes a related view psychometrically. XEQ measures four dimensions—Learning, Utility, Fulfilment, and Engagement—using 18 Likert-type statements rated from 1 to 5. For participant \(j\), the total score is
\[
r_j=\sum_{i=1}^{18} r^i_j,
\]
with overall mean per item \(\bar r_j=r_j/18\). The paper reports content validation with 13 XAI experts, a Scale-Level Average Content Validity Index \(S\text{-}CVI(a)=0.8846\), internal consistency with all item-total correlations \(iT_i\ge 0.50\), and Cronbach’s \(\alpha=0.9562\). In the pilot study, linear discriminant analysis achieved classification accuracy \(0.63\pm0.05\) and macro-\(F_1\) \(0.63\pm0.05\), while ANOVA comparing mean totals yielded \(p=1.63\times10^{-12}\) and Cohen’s \(d=1.76\) [2407.10662].

Taken together, these frameworks formalize a central H-XAI claim: no single evaluation dimension captures all explanation desiderata. A plausible implication is that H-XAI evaluation is less a replacement for faithfulness metrics than an attempt to place them within a larger measurement ecology.

## 4. Architectures and formal mechanisms

Several papers give explicit formalizations of Holistic-XAI systems. The LLM-based democratization framework defines a mapping
\[
F_\theta:(\mathbb{K},D,x^\*,M(x^\*))\to (e^\*_{\mathrm{tech}},e^\*_{\mathrm{lay}})
\]
where \(\mathbb{K}=\{K_{\mathrm{dom}},K_{\mathrm{xai}}\}\) contains domain-relevant and explainability-principles knowledge, \(D\) is a small in-context demonstration set, \(e^\*_{\mathrm{tech}}\) is a technical explanation targeted at experts, and \(e^\*_{\mathrm{lay}}\) is a human-centered narrative targeted at non-experts. The framework keeps \(\theta\) fixed and instead optimizes the demonstration set to maximize Spearman rank correlation to ground-truth explanations subject to readability and consistency constraints [2506.12240]. Its grounding mechanism is a “contextual thesaurus” built from more than 40 data, model, and XAI combinations.

The three-layer HCXAI framework provides a different formal decomposition:
\[
(y,e_1)=M_\theta(x), \qquad
e_2=\Phi_{\rm HCAI}(e_1,u_{\rm profile},c_{\rm load}), \qquad
\theta,\Phi_{\rm HCAI}\leftarrow \Phi_{\rm FB}(e_2,f).
\]
Here Layer 1 produces a base decision and basic explanation, Layer 2 adapts the explanation to user expertise and cognitive context, and Layer 3 incorporates feedback to refine both the model and the explanation generator [2504.13926]. This is one of the clearest formulations of adaptation and feedback as first-class components rather than interface add-ons.

The interactive multi-method H-XAI framework formalizes explanation as a workflow in which stakeholder queries are mapped either to causal ratings or to post-hoc XAI. Its causal rating module returns Weighted Rejection Score, Average Treatment Effect, or Deconfounded Impact Estimation depending on the query, and compares these scores with a random baseline and a biased baseline [2508.05792]. This architecture makes hypothesis testing explicit: stakeholders ask questions, instantiate variables such as \(T\), \(O\), and \(Z\), compare the system with baselines, and iterate.

The workflow-wide HXAI perspective generalizes still further. It defines \(E_{\mathrm{HXAI}}:(U,C,P,O)\to X\), where \(U\) is user profile, \(C\) is the six-component workflow taxonomy, \(P\) is the ML pipeline, \(O\) denotes opaque artifacts, and \(X\) is the set of human-readable, user-tailored explanations. Its communication layer is an LLM-powered agent that aggregates outputs from data explainability, analysis setup explainability, learning process explainability, model output explainability, and model quality explainability [2508.11529].

## 5. Representative applications and empirical demonstrations

The SIDU-TXT study illustrates H-XAI as a rigorous assessment protocol for NLP explanations. SIDU-TXT extends the image-domain SIDU method to textual data using feature activation maps from a black-box model to generate word-level heatmaps. On IMDB sentiment analysis, the paper reports insertion AUC \(0.5513\) for SIDU-TXT versus \(0.4308\) for Grad-CAM and \(0.4228\) for LIME, and deletion AUC \(0.1537\) versus \(0.2073\) and \(0.2431\), respectively. At the sentence level, SIDU-TXT achieved precision \(0.570\), recall \(0.515\), and \(F_1\) \(0.504\), exceeding the reported Grad-CAM and LIME values. In the asylum-decision domain, however, SIDU-TXT and Grad-CAM were comparable, and both fell short of fully satisfying expert expectations [2402.03043]. The application is important because it shows that strong functionally grounded and human grounded results do not eliminate domain-specific shortcomings.

The LLM-based H-XAI framework demonstrates a dual-layer explanation system in a well-being clustering scenario. Its few-shot configuration with LLaMA3 produced Spearman rank correlation of approximately \(0.92\) against LIME ground truth, while zero-shot performance was approximately \(0.01\). As shot count increased, NDCG difference decreased from \(0.07\) to \(0.001\), and Euclidean distance from \(0.32\) to \(0.02\). In a user study with \(N=56\), pragmatic quality in a paired comparison was \(+1.00\) for the framework versus \(-0.16\) for LIME, with \(p<0.01\) [2506.12240]. These results support the specific H-XAI claim that the same system response can simultaneously preserve technical fidelity and improve human-friendliness.

The interactive causal-plus-XAI framework is demonstrated in binary credit risk classification and financial time-series forecasting. In the German Credit case, it compares Logistic Regression and Random Forest and reports RF accuracy \(0.78\) versus LR \(0.75\), with \(\mathrm{DIE\%}(\mathrm{Age})\) equal to \(12\%\) in RF and \(5\%\) in LR. In financial forecasting, it evaluates MOMENT, Gemini, and ARIMA; for example, \(\mathrm{ATE}(\mathrm{Perturb}=\mathrm{missing})\) is \(0.12\) in MOMENT and \(0.18\) in Gemini, while ARIMA has \(\mathrm{ATE}=0.22\) and \(\mathrm{DIE\%}=2\%\). The case studies are notable because they place local explanations, global summaries, causal ratings, and baseline comparisons within one stakeholder-driven workflow [2508.05792].

In medicine, xHAIM presents a clinically oriented holistic explainable AI pipeline with four structured steps: automatically identifying task-relevant patient data across modalities, generating comprehensive patient summaries, using these summaries for predictive modeling, and providing clinical explanations linked to patient-specific medical knowledge. On the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from \(79.9\%\) to \(90.3\%\), an absolute gain of \(10.4\) percentage points with \(p<0.001\), and produces narrative explanations grounded by anchored citations to original report chunks [2507.00205]. This application shows that, in some domains, “holistic” also means integrating task-aware retrieval, summarization, prediction, and traceable explanation.

## 6. Limitations, misconceptions, and open directions

A recurring misconception is that H-XAI is merely “more explanation.” The literature instead frames it as a change in explanatory unit: from isolated artifacts to experiences, workflows, or stakeholder-specific inquiry processes. Another misconception is that holistic approaches abandon rigor. The evaluation literature argues the opposite, namely that rigor requires combining functionally grounded, human grounded, and application grounded evidence, or combining psychometric validation with objective metrics [2402.03043; 2407.10662].

The limitations are substantial. Application-grounded studies are described as costly and hard to organize, and expert studies remain time-consuming [2402.03043]. The LLM-based H-XAI framework reports that hallucinations persist under zero-shot prompting, that few-shot learning reduces but does not eliminate errors, and that consistency across multi-turn explanations remains an open question [2506.12240]. The causal-plus-XAI framework requires a causal graph, relies on synthetic baselines, and may incur heavy computation for ATE and DIE% estimation at scale [2508.05792]. The workflow-wide HXAI survey identifies coverage gaps across existing tools, especially for Data, Learning Process, Analysis Setup, and root-cause advice [2508.11529]. More speculative multidisciplinary formulations add further open problems concerning generative model explainability, privacy versus transparency, concept scaling, and interdisciplinary barriers [2402.06673].

Future directions in the literature are correspondingly broad. They include broader validation of XEQ in medical imaging and other stakeholder groups, an XEQ benchmark and public analysis tool, adaptation of SIDU-TXT to transformer-based models, richer multi-turn and rule-based explanation support in LLM-based H-XAI, extension of medical pipelines to federated settings and external cohorts, and the development of explanation systems that remain interactive, cognitively manageable, and role-aware across the full ML lifecycle [2407.10662; 2402.03043; 2506.12240; 2507.00205; 2508.11529].

Within contemporary arXiv literature, Holistic-XAI therefore designates a broad attempt to move explainability beyond local feature relevance alone and toward systems that are multi-method, stakeholder-sensitive, workflow-aware, and empirically evaluable at multiple levels. Its unifying thesis is not that one explanatory form can satisfy every requirement, but that trustworthy explanation in practice must integrate fidelity, human interpretation, interaction quality, contextual relevance, and, increasingly, traceability across the whole decision process.

Source: https://www.emergentmind.com/topics/holistic-xai-h-xai