Cover Image

Explainable AI: Learning from the Learners

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Vinuesa, Ricardo, Brunton, Steven L., Mengaldo, Gianmarco
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence Machine Learning Computational Physics Physics and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dynamical errors in machine learning forecasts
by: Fang, Zhou, et al.
Published: (2025)

Explainability matters: The effect of liability rules on the healthcare sector
by: Wei, Jiawen, et al.
Published: (2025)

Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence
by: Mengaldo, Gianmarco
Published: (2024)

ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics
by: Varshney, Deeksha, et al.
Published: (2025)

AI and Machine Learning for Next Generation Science Assessments
by: Zhai, Xiaoming
Published: (2024)

Deep Learning Opacity in Scientific Discovery
by: Duede, Eamon
Published: (2022)

Exploring utilization of generative AI for research and education in data-driven materials science
by: Misawa, Takahiro, et al.
Published: (2025)

Predicting Healthcare Provider Engagement in SMS Campaigns
by: Qureshi, Daanish Aleem, et al.
Published: (2025)

Transparent AI: The Case for Interpretability and Explainability
by: Ramachandram, Dhanesh, et al.
Published: (2025)

eXIAA: eXplainable Injections for Adversarial Attack
by: Pesce, Leonardo, et al.
Published: (2025)

Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle
by: Deck, Luca, et al.
Published: (2024)

FNDEX: Fake News and Doxxing Detection with Explainable AI
by: Sallami, Dorsaf, et al.
Published: (2024)

Revisiting the robustness of post-hoc interpretability methods
by: Wei, Jiawen, et al.
Published: (2024)

Beyond MMD: Evaluating Graph Generative Models with Geometric Deep Learning
by: Romano, Salvatore, et al.
Published: (2025)

Explainable AI Systems Must Be Contestable: Here's How to Make It Happen
by: Moreira, Catarina, et al.
Published: (2025)

BERT-Based Approach for Automating Course Articulation Matrix Construction with Explainable AI
by: Shiferaw, Natenaile Asmamaw, et al.
Published: (2024)

Explainable AI for Mental Health Emergency Returns: Integrating LLMs with Predictive Modeling
by: Ahmed, Abdulaziz, et al.
Published: (2025)

Explainable AI for Predicting and Understanding Mathematics Achievement: A Cross-National Analysis of PISA 2018
by: Liu, Liu, et al.
Published: (2025)

3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
by: Zhang, Liang, et al.
Published: (2024)

Learning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education
by: Nusrat, Mary, et al.
Published: (2026)

Adopting Explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climates
by: Eshraghi, Pegah, et al.
Published: (2024)

Initialisation and Network Effects in Decentralised Federated Learning
by: Badie-Modiri, Arash, et al.
Published: (2024)

PhysGym: Benchmarking LLMs in Interactive Physics Discovery with Controlled Priors
by: Chen, Yimeng, et al.
Published: (2025)

Social Physics Informed Diffusion Model for Crowd Simulation
by: Chen, Hongyi, et al.
Published: (2024)

Task complexity shapes internal representations and robustness in neural networks
by: Jankowski, Robert, et al.
Published: (2025)

Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
by: Kim, Jiyeon, et al.
Published: (2025)

Progress in Artificial Intelligence and its Determinants
by: Douglas, Michael R., et al.
Published: (2025)

PANDORA: Deep graph learning based COVID-19 infection risk level forecasting
by: Yu, Shuo, et al.
Published: (2024)

CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints
by: Wang, Xin, et al.
Published: (2025)

Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data
by: Nolte, Henrik, et al.
Published: (2025)

Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models
by: Kolic, Blas, et al.
Published: (2025)

MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling
by: Krishnan, Dhriti, et al.
Published: (2026)

Interdisciplinary Expertise to Advance Equitable Explainable AI
by: Bennett, Chloe R., et al.
Published: (2024)

Hierarchy of extreme-event predictability in turbulence revealed by machine learning
by: Yang, Yuxuan, et al.
Published: (2026)

Bias and Identifiability in the Bounded Confidence Model
by: Borile, Claudio, et al.
Published: (2025)

Revolutionising Distance Learning: A Comparative Study of Learning Progress with AI-Driven Tutoring
by: Möller, Moritz, et al.
Published: (2024)

SHAP Distance: An Explainability-Aware Metric for Evaluating the Semantic Fidelity of Synthetic Tabular Data
by: Yu, Ke, et al.
Published: (2025)

Exam Readiness Index (ERI): A Theoretical Framework for a Composite, Explainable Index
by: Verma, Ananda Prakash
Published: (2025)

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned
by: Schweighofer, Kajetan, et al.
Published: (2025)

SpecReX: Explainable AI for Raman Spectroscopy
by: Blake, Nathan, et al.
Published: (2025)