Explainable AI: Learning from the Learners

2026-01-09

arXiv.org

AI Summary

This work proposes a unified framework grounded in explainable artificial intelligence (XAI) to address the opacity of AI systems, which undermines trust and accountability in high-stakes scientific and engineering applications. By integrating causal reasoning with foundation models, the approach enables “learning from the learner”—extracting causal mechanisms directly from AI systems to inform robust design and control strategies. This paradigm significantly enhances the trustworthy collaboration of AI in scientific discovery, optimization, and certification. The study further delineates critical challenges and pathways to advancement concerning fidelity, generalizability, and usability, offering a systematic perspective on how interpretable, causally grounded AI can bridge the gap between performance and reliability in real-world deployment.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Reasoning under Uncertainty: Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Artificial intelligence now outperforms humans in several scientific and engineering tasks, yet its internal representations often remain opaque. In this Perspective, we argue that explainable artificial intelligence (XAI), combined with causal reasoning, enables {\it learning from the learners}. Focusing on discovery, optimization and certification, we show how the combination of foundation models and explainability methods allows the extraction of causal mechanisms, guides robust design and control, and supports trust and accountability in high-stakes applications. We discuss challenges in faithfulness, generalization and usability of explanations, and propose XAI as a unifying framework for human-AI collaboration in science and engineering.

Problem

• Explainable AI
• causal reasoning
• trust
• accountability
• human-AI collaboration

Innovation

• Explainable AI
• Causal Reasoning
• Foundation Models
• Human-AI Collaboration
• Trust and Accountability