面向基础模型时代的社会机器人：伦理且用户自适应的可解释性设计
摘要
基础模型正越来越多地嵌入到社会机器人中， 不仅调节 what they say and do， 还调节 how they adapt to users over time。这种转变使得传统的“通用方案”解释策略尤其problematic： generic justifications are now wrapped around behaviour produced by models trained on vast, heterogeneous, and opaque datasets。我们认为，伦理且用户自适应的可解释性 must be treated as a core design objective for foundation-model-driven social robotics。我们 first identify open challenges around explainability and ethical concerns that arise when both adaptation and explanation are delegated to foundation models。 Building on this analysis， we propose four recommendations for moving towards user-adapted， modality-aware， and co-designed explanation strategies grounded in smaller， fairer datasets。 An illustrative use case of an LLM-driven socially assistive robot demonstrates how these recommendations might be instantiated in a sensitive， real-world domain。
引用
@article{arxiv.2603.00102,
title = {Designing Social Robots with Ethical, User-Adaptive Explainability in the Era of Foundation Models},
author = {Fethiye Irmak Dogan and Alva Markelius and Hatice Gunes},
journal= {arXiv preprint arXiv:2603.00102},
year = {2026}
}
备注
Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction