arXiv cs.AI· Nishtha Madaan, Srikanta Bedathur·· 13 小时前AI 评分26
DDAR：无需人工成本标注的数据驱动可执行补救方法
Follow the Herd: A Data-Driven Approach for Actionable Recourse
AI 导读
研究者提出 Data-Driven Action Recourse（DDAR），首个无需人工标注动作成本即可提供可执行补救的方法。DDAR 基于历史用户数据中高概率轨迹应对应更低成本这一假设，先构建用户轨迹的动力学模型，再在该模型下搜索能达成目标结果的轨迹。实验显示，该方法无需人工监督即可给出合理的补救建议，且动作成本低于现有方法。
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