_version_ 1866914598620233728
author Afroogh, Saleh
Ahmed, Syed Ishtiaque
Ahrweiler, Petra
Alvarez-Melis, David
Arief, Mansur Maturidi
Barakova, Emilia
Bargagli-Stoffi, Falco J.
Biyik, Erdem
Chen, Hanjie
Chen, Xiang 'Anthony'
Clements, Robert Alan
Crockett, Keeley
Dhurandhar, Amit
Dogan, Fethiye Irmak
Dollinger, Mollie
Eslami, Motahhare
Faisal, Aldo A
Farahi, Arya
Pradier, Melanie F.
Gabriel, Saadia
Garcia-Olano, Diego
Ghassemi, Marzyeh
Ghosh, Shaona
Gunes, Hatice
Hajiramezanali, Ehsan
Haufe, Stefan
Huang, Biwei
Hwang, Angel
Islam, Md Tauhidul
Jiao, Junfeng
Karimi, Amir-Hossein
Kazeminasab, Saber
Kuzminykh, Anastasia
La Cava, William
Lim, Brian Y.
Liu, Xiaofeng
Mofrad, Mohammad R. K.
Parrish, Alicia
Perez-Ortiz, Maria
Raj, Shriti
Swayamdipta, Swabha
Talebi, Salmonn
Varshney, Kush R.
Vorvoreanu, Mihaela
Weng, Lily
Xiang, Alice
Xu, Yiming
Zhao, Ding
Zhao, Jieyu
author_facet Afroogh, Saleh
Ahmed, Syed Ishtiaque
Ahrweiler, Petra
Alvarez-Melis, David
Arief, Mansur Maturidi
Barakova, Emilia
Bargagli-Stoffi, Falco J.
Biyik, Erdem
Chen, Hanjie
Chen, Xiang 'Anthony'
Clements, Robert Alan
Crockett, Keeley
Dhurandhar, Amit
Dogan, Fethiye Irmak
Dollinger, Mollie
Eslami, Motahhare
Faisal, Aldo A
Farahi, Arya
Pradier, Melanie F.
Gabriel, Saadia
Garcia-Olano, Diego
Ghassemi, Marzyeh
Ghosh, Shaona
Gunes, Hatice
Hajiramezanali, Ehsan
Haufe, Stefan
Huang, Biwei
Hwang, Angel
Islam, Md Tauhidul
Jiao, Junfeng
Karimi, Amir-Hossein
Kazeminasab, Saber
Kuzminykh, Anastasia
La Cava, William
Lim, Brian Y.
Liu, Xiaofeng
Mofrad, Mohammad R. K.
Parrish, Alicia
Perez-Ortiz, Maria
Raj, Shriti
Swayamdipta, Swabha
Talebi, Salmonn
Varshney, Kush R.
Vorvoreanu, Mihaela
Weng, Lily
Xiang, Alice
Xu, Yiming
Zhao, Ding
Zhao, Jieyu
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2602_24176
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Afroogh, Saleh
Ahmed, Syed Ishtiaque
Ahrweiler, Petra
Alvarez-Melis, David
Arief, Mansur Maturidi
Barakova, Emilia
Bargagli-Stoffi, Falco J.
Biyik, Erdem
Chen, Hanjie
Chen, Xiang 'Anthony'
Clements, Robert Alan
Crockett, Keeley
Dhurandhar, Amit
Dogan, Fethiye Irmak
Dollinger, Mollie
Eslami, Motahhare
Faisal, Aldo A
Farahi, Arya
Pradier, Melanie F.
Gabriel, Saadia
Garcia-Olano, Diego
Ghassemi, Marzyeh
Ghosh, Shaona
Gunes, Hatice
Hajiramezanali, Ehsan
Haufe, Stefan
Huang, Biwei
Hwang, Angel
Islam, Md Tauhidul
Jiao, Junfeng
Karimi, Amir-Hossein
Kazeminasab, Saber
Kuzminykh, Anastasia
La Cava, William
Lim, Brian Y.
Liu, Xiaofeng
Mofrad, Mohammad R. K.
Parrish, Alicia
Perez-Ortiz, Maria
Raj, Shriti
Swayamdipta, Swabha
Talebi, Salmonn
Varshney, Kush R.
Vorvoreanu, Mihaela
Weng, Lily
Xiang, Alice
Xu, Yiming
Zhao, Ding
Zhao, Jieyu
Computers and Society
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.
title Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
topic Computers and Society
url https://arxiv.org/abs/2602.24176