Pith Number

pith:C7TWP67Y

pith:2026:C7TWP67YQHFS5ZVC6SOYJYYCE4

not attested not anchored not stored refs pending

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

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

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

arxiv:2602.24176 v5 · 2026-02-27 · cs.CY

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

The assumption that the identified symptoms stem from deeper root causes (two paradoxes, two confusions, five false assumptions) that cannot be resolved through incremental improvements to existing XAI methods and instead require a complete four-pronged paradigm shift.

C3one line summary

Current XAI methods for DNNs and LLMs rest on paradoxes and false assumptions that demand a paradigm shift to verification protocols, scientific foundations, context-aware design, and faithful model analysis rather than post-hoc explanations.

Cited by

1 paper in Pith

Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis

Receipt and verification

First computed	2026-05-26T02:05:08.021048Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

17e767fbf881cb2ee6a2f49d84e3022722b2957b20d2cf1593256cf61d62639f

Aliases

arxiv: 2602.24176 · arxiv_version: 2602.24176v5 · doi: 10.48550/arxiv.2602.24176 · pith_short_12: C7TWP67YQHFS · pith_short_16: C7TWP67YQHFS5ZVC · pith_short_8: C7TWP67Y

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/C7TWP67YQHFS5ZVC6SOYJYYCE4 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 17e767fbf881cb2ee6a2f49d84e3022722b2957b20d2cf1593256cf61d62639f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "95dc9b8a0a7d72731d4f561a505beee94eb56cef756b0b439fddda0c2b92588c",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CY",
    "submitted_at": "2026-02-27T16:58:27Z",
    "title_canon_sha256": "ea83809915659cf942913665cb2798e85122379a6e9facb388f21c50d18eafb3"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.24176",
    "kind": "arxiv",
    "version": 5
  }
}