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arxiv: 2602.24176 · v5 · pith:C7TWP67Ynew · submitted 2026-02-27 · 💻 cs.CY

Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions

Saleh Afroogh , Syed Ishtiaque Ahmed , Petra Ahrweiler , David Alvarez-Melis , Mansur Maturidi Arief , Emilia Barakova , Falco J. Bargagli-Stoffi , Erdem Biyik
show 41 more authors
Hanjie Chen Xiang 'Anthony' Chen Robert Alan Clements Keeley Crockett Amit Dhurandhar Fethiye Irmak Dogan Mollie Dollinger Motahhare Eslami Aldo A Faisal Arya Farahi Melanie F. Pradier Saadia Gabriel Diego Garcia-Olano Marzyeh Ghassemi Shaona Ghosh Hatice Gunes Ehsan Hajiramezanali Stefan Haufe Biwei Huang Angel Hwang Md Tauhidul Islam Junfeng Jiao Amir-Hossein Karimi Saber Kazeminasab Anastasia Kuzminykh William La Cava Brian Y. Lim Xiaofeng Liu Mohammad R. K. Mofrad Alicia Parrish Maria Perez-Ortiz Shriti Raj Swabha Swayamdipta Salmonn Talebi Kush R. Varshney Mihaela Vorvoreanu Lily Weng Alice Xiang Yiming Xu Ding Zhao Jieyu Zhao
This is my paper

Pith reviewed 2026-05-15 18:47 UTC · model grok-4.3

classification 💻 cs.CY
keywords Explainable AIPost-XAIParadigm shiftDeep neural networksLarge language modelsAI verificationInterpretabilityCertified AI
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The pith

XAI contains deep paradoxes and false assumptions that make incremental fixes counterproductive, requiring a full shift to certified AI approaches.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines limitations in explainable AI for deep neural networks and large language models, tracing symptoms to two paradoxes, two conceptual confusions, and five false assumptions. These lead to the claim that XAI is experimentally flawed, conceptually inconsistent, and that attempts to repair it only deepen the problems. A sympathetic reader would care because current explainability efforts risk producing misleading or confusing outputs instead of reliable understanding. The authors therefore call for a four-pronged replacement: verification-focused interactive AI, AI epistemology, user-sensible AI, and model-centered interpretability. This reorients the field from post-hoc explanations toward scientific certification and context-aware design.

Core claim

The central claim is that current XAI approaches for DNNs and LLMs exhibit significant empirical flaws, rest on conceptual paradoxes, and that further reform efforts would worsen confusion; therefore the field must undertake a four-pronged paradigm shift to verification-focused Interactive AI for community certification protocols, AI Epistemology for rigorous foundations, User-Sensible AI for context-aware tailoring, and Model-Centered Interpretability for faithful technical analysis, together enabling reliable and certified AI development.

What carries the argument

The four-pronged paradigm shift that replaces post-hoc explanation with verification protocols, epistemic foundations, community-specific design, and direct model analysis.

If this is right

  • AI performance certification would shift from post-hoc explanations to community-established verification protocols.
  • Research would prioritize building scientific foundations through AI epistemology rather than ad-hoc interpretability techniques.
  • Systems would be designed as user-sensible from the start, adapting to the needs of specific user communities.
  • Technical analysis would center on the models themselves for faithful description instead of generating separate explanations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Regulatory frameworks might move toward mandatory verification benchmarks rather than explainability requirements.
  • User studies in human-AI interaction could test whether the new directions reduce documented confusion compared with current XAI outputs.
  • Connections to philosophy of science become relevant for validating what counts as certified knowledge in AI systems.

Load-bearing premise

The assumption that the identified paradoxes and false assumptions cannot be resolved by improving existing XAI methods and instead require abandoning the explainability paradigm altogether.

What would settle it

A concrete demonstration that an existing XAI technique can be adjusted to eliminate the stated paradoxes and confusions while still delivering consistent, non-misleading explanations across multiple user studies and model types.

read the original abstract

This study provides a cross-disciplinary examination of Explainable Artificial Intelligence (XAI) approaches-focusing on deep neural networks (DNNs) and large language models (LLMs)-and identifies empirical and conceptual limitations in current XAI. We discuss critical symptoms that stem from deeper root causes (i.e., two paradoxes, two conceptual confusions, and five false assumptions). These fundamental problems within the current XAI research field reveal three insights: experimentally, XAI exhibits significant flaws; conceptually, it is paradoxical; and pragmatically, further attempts to reform the paradoxical XAI might exacerbate its confusion-demanding fundamental shifts and new research directions. To move beyond XAI's limitations, we propose a four-pronged synthesized paradigm shift toward reliable and certified AI development. These four components include: verification-focused Interactive AI (IAI) to establish scientific community protocols for certifying AI system performance rather than attempting post-hoc explanations, AI Epistemology for rigorous scientific foundations, User-Sensible AI to create context-aware systems tailored to specific user communities, and Model-Centered Interpretability for faithful technical analysis-together offering comprehensive post-XAI research directions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript provides a cross-disciplinary examination of XAI approaches for DNNs and LLMs, identifies empirical and conceptual limitations stemming from two paradoxes, two confusions, and five false assumptions, derives three insights (experimental flaws, conceptual paradox, and risk that reform exacerbates confusion), and proposes a four-pronged paradigm shift to post-XAI directions: verification-focused Interactive AI (IAI), AI Epistemology, User-Sensible AI, and Model-Centered Interpretability.

Significance. If the root-cause diagnosis holds and the necessity of abandoning incremental XAI improvements is established, the work could redirect research toward certified, context-aware, and epistemologically grounded AI systems. The proposal of concrete post-XAI components such as IAI and User-Sensible AI offers a structured research agenda, but the manuscript's support remains limited to conceptual assertion without empirical studies, formal derivations, or case analyses demonstrating that targeted fixes must fail.

major comments (2)
  1. [Abstract and section on the three insights] The pragmatic insight that further reform of XAI would exacerbate confusion (stated in the abstract and the section deriving the three insights) is asserted without a concrete demonstration. No argument shows that any incremental change addressing one of the listed paradoxes (e.g., the explanation paradox) necessarily leads to contradiction or performance regression within existing XAI frameworks.
  2. [Section identifying root causes (two paradoxes, two confusions, five false assumptions)] The inference that the two paradoxes, two confusions, and five false assumptions are irresolvable incrementally and therefore require a complete four-pronged shift is not derived. The root-causes section lists these issues but provides no formal conditions or counter-example showing that any fix satisfying those conditions must fail, leaving the necessity claim unsupported.
minor comments (2)
  1. [Proposal of the four-pronged paradigm shift] The newly introduced terms 'verification-focused Interactive AI (IAI)' and 'User-Sensible AI' are presented as distinct components but lack precise operational definitions or explicit contrasts with prior interactive or user-centered AI literature.
  2. [Discussion of experimental flaws] The claim that XAI 'exhibits significant flaws' experimentally would be strengthened by citing specific evaluation studies or benchmarks rather than remaining at the level of general assertion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and detailed report. The comments highlight opportunities to strengthen the logical derivations in our conceptual analysis. Below we respond point by point to the major comments, clarifying how the manuscript derives its claims from the identified root causes while indicating targeted revisions that will make the necessity argument more explicit without altering the paper's cross-disciplinary scope.

read point-by-point responses

  1. Referee: [Abstract and section on the three insights] The pragmatic insight that further reform of XAI would exacerbate confusion (stated in the abstract and the section deriving the three insights) is asserted without a concrete demonstration. No argument shows that any incremental change addressing one of the listed paradoxes (e.g., the explanation paradox) necessarily leads to contradiction or performance regression within existing XAI frameworks.

    Authors: The pragmatic insight follows directly from the explanation paradox as defined in the root-causes section: any post-hoc method that increases local fidelity necessarily reduces human-comprehensible structure because DNN/LLM decision boundaries are high-dimensional and non-linear. The manuscript supports this by tracing how successive XAI refinements (e.g., from LIME to SHAP to attention visualization) have each traded one desideratum for another, producing the documented increase in contradictory claims across the literature. While the current text presents this as a logical consequence rather than a formal proof, we agree that an explicit chain of implications would strengthen the claim. We will therefore insert a short subsection that enumerates three representative incremental proposals, shows the specific contradiction each creates with one of the five false assumptions, and notes the resulting performance or interpretability regression reported in the cited empirical studies. revision: yes

  2. Referee: [Section identifying root causes (two paradoxes, two confusions, five false assumptions)] The inference that the two paradoxes, two confusions, and five false assumptions are irresolvable incrementally and therefore require a complete four-pronged shift is not derived. The root-causes section lists these issues but provides no formal conditions or counter-example showing that any fix satisfying those conditions must fail, leaving the necessity claim unsupported.

    Authors: The necessity claim is derived by showing that each root cause violates an invariant property of DNNs and LLMs (opacity, lack of causal semantics, and absence of a shared scientific ontology). Because these invariants are preserved under any post-hoc or architectural patch that leaves the model class unchanged, no incremental fix can simultaneously satisfy all five false assumptions without reintroducing at least one paradox. The manuscript illustrates this through the two confusions (equating correlation with explanation, and treating user mental models as model-agnostic). To make the derivation more transparent, we will add a compact table that maps each false assumption to the invariant it contradicts and to the paradox it re-creates, thereby supplying the explicit conditions the referee requests. revision: yes

Circularity Check

0 steps flagged

No circularity: conceptual critique relies on external literature analysis

full rationale

The paper conducts a cross-disciplinary literature review to identify symptoms in XAI (flaws, paradoxes, confusions, false assumptions) and argues these necessitate a four-pronged paradigm shift. No mathematical derivations, equations, fitted parameters, or self-referential definitions appear in the provided text. The central inference to post-XAI directions follows from logical examination of existing practices rather than any reduction of outputs to inputs by construction. Self-citations, if present among the large author list, are not shown to be load-bearing for the root-cause diagnosis or the shift proposal. The argument is self-contained against external benchmarks in the XAI literature and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on the premise that XAI limitations are structural and require a full paradigm shift, drawing from cross-disciplinary conceptual analysis without new empirical data or formal proofs.

axioms (2)
  • domain assumption Post-hoc explanations cannot provide certification of AI system performance.
    Invoked to justify the shift to verification-focused IAI.
  • domain assumption AI requires rigorous scientific foundations analogous to established sciences.
    Basis for the AI Epistemology component.
invented entities (2)
  • verification-focused Interactive AI (IAI) no independent evidence
    purpose: Establish scientific community protocols for certifying AI system performance rather than post-hoc explanations.
    One of the four proposed research directions introduced as alternative to current XAI.
  • User-Sensible AI no independent evidence
    purpose: Create context-aware systems tailored to specific user communities.
    One of the four proposed research directions.

pith-pipeline@v0.9.0 · 5757 in / 1623 out tokens · 59468 ms · 2026-05-15T18:47:47.279359+00:00 · methodology

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