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; 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

arXiv · 2602.24176

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

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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.

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Saleh Afroogh, Syed Ishtiaque Ahmed, Petra Ahrweiler, David Alvarez-Melis, Mansur Maturidi Arief, Emilia Barakova, Falco J. Bargagli-Stoffi, Erdem Biyik, 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. 2026-02-27. Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions. https://arxiv.org/abs/2602.24176

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