用学习理论提升AI解释的人性化设计，增强人类掌控力。
Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges
- 将学习理论融入XAI全流程，以学习者为中心设计解释
- 强调解释应促进人类理解与能力成长，而非仅技术透明
- 适合关注人机协同、AI可解释性设计的研究者
随着人工智能系统规模与复杂性的持续增长，实现AI透明性的难度日益加大。在大型模型与复杂AI系统的背景下，我们为何需要解释AI？又该解释什么？尽管解释具有多重功能，但面对复杂性，人类始终依赖解释来促进学习。本文提出，学习理论可深度融入XAI生命周期，推动以学习者为中心的解释评估、设计与评价。基于已有研究，我们认为采用学习者中心视角的XAI，有助于增强人类自主性，降低解释风险，推动以人为本的XAI实践演进。
原文摘要 · Abstract (English)
As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face of complexity humans have used and continue to use explanations to foster learning. In this position paper, we discuss how learning theories can be infused in the XAI lifecycle, as well as the key opportunities and challenges when adopting a learner-centered approach to assess, design and evaluate AI explanations. Building on past work, we argue that a learner-centered approach to Explainable AI (XAI) can enhance human agency and ease XAI risks mitigation, helping evolve the practice of human-centered XAI.
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