arXiv:2503.04343cs.CYcs.AI2025-03被引 1
让人类解释判断，让AI更懂人话
Talking Back -- human input and explanations to interactive AI systems
- 人类用解释反馈引导AI学习
- 模型生成的判断更贴近人类认知
- 适合构建可交互的智能系统
虽然可解释AI（XAI）关注如何向人类提供解释，但反过来——人类通过解释其判断来指导AI——是否能促成更丰富、协同的人机系统？本文探讨了多种人类输入形式，研究人类解释如何引导机器学习模型，使其自动化判断与解释更贴近人类概念。该方法有望提升人机协作的语义对齐性，推动更具交互性的智能系统发展。
原文摘要 · Abstract (English)
While XAI focuses on providing AI explanations to humans, can the reverse - humans explaining their judgments to AI - foster richer, synergistic human-AI systems? This paper explores various forms of human inputs to AI and examines how human explanations can guide machine learning models toward automated judgments and explanations that align more closely with human concepts.
人机交互可解释性反馈机制
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