Follow the Herd：数据驱动的可操作行动建议方法 DDAR
Follow the Herd: A Data-Driven Approach for Actionable Recourse
AI 导读
arXiv 论文提出「数据驱动行动建议」方法 DDAR，首次无需人工手工设计行动成本函数即可为用户提供可操作的建议。其核心思路是历史用户数据中高概率轨迹对应更低成本，先构建用户轨迹的动力学模型，再在该模型下搜索能达到目标结果的轨迹。实验显示该方法无需人工监督即可给出合理建议，且行动成本低于现有方法。
正文 · 原文
Abstract:As machine learning systems make increasingly greater decisions about people's lives, it is critical to provide users with actionable recourse about how they can achieve their desired outcomes. This problem is currently tackled by relying on expensive human supervision which involves hand-designing a cost function before searching for low-cost actions that achieve the desired outcome. In this paper, we propose \textit{``Data-Driven Action Recourse"} (DDAR), the first approach for providing action recourse without requiring costly supervision from humans about action costs. We build upon the notion that highly likely trajectories in the historical user data should be associated with lower costs. With this, DDAR first builds a dynamics model of historical user trajectories. Under this model, it then searches for trajectories that achieve the desired outcome. In experiments, we find that this simple approach recommends plausible action recourse without requiring human supervision, achieving lower action costs than the existing approaches.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36580 [cs.AI] |
| (or arXiv:2610.36580v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36580 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nishtha Madaan [view email]
[v1]
Tue, 27 Oct 2026 03:36:02 UTC (904 KB)
来源：arXiv cs.AI · arxiv.org