让社交机器人解释行为时兼顾伦理与用户差异，避免千篇一律的说辞。
Designing Social Robots with Ethical, User-Adaptive Explainability in the Era of Foundation Models
- 用小而公平的数据集，设计能随用户调整的解释机制。
- 提出四条建议，使解释更适配用户、模态和场景。
- 适合关注机器人伦理、个性化交互的研究者与开发者。
基础模型正被广泛用于社交机器人，不仅决定其言行，还影响其对用户的长期适应。这一转变使得传统的“一刀切”解释策略问题凸显：通用解释如今包裹在由海量、异构且不透明数据训练出的模型行为之上。我们主张，基于基础模型的社交机器人必须将伦理性和用户自适应解释作为核心设计目标。首先，我们识别出当适应与解释均由基础模型承担时引发的可解释性与伦理挑战。在此基础上，提出四项建议，推动建立基于更小、更公平数据集的用户适配、模态感知、协同设计的解释策略。一个基于大语言模型的社服机器人应用案例展示了这些建议在敏感真实场景中的实现可能。
原文摘要 · Abstract (English)
Foundation models are increasingly embedded in social robots, mediating not only what they say and do but also how they adapt to users over time. This shift renders traditional ``one-size-fits-all'' explanation strategies especially problematic: generic justifications are now wrapped around behaviour produced by models trained on vast, heterogeneous, and opaque datasets. We argue that ethical, user-adapted explainability must be treated as a core design objective for foundation-model-driven social robotics. We 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.
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