让AI决策更可辩论：通过调整权重使关键论点达到期望强度
Contestability in Quantitative Argumentation
- 基于梯度的敏感性分析，量化每条论证边对目标论点的影响
- 提出迭代算法，逐步调整边权重实现目标论点强度
- 适用于个性化推荐等需可解释决策的AI系统
可辩论的AI要求其决策符合人类偏好。尽管多种论证形式已被证明能支持可辩论性，但加权定量双极论证框架（EW-QBAFs）仍缺乏关注。本文提出针对EW-QBAFs的可辩论性问题：如何修改边权重（如偏好）以使特定论点（即主题论点）达到期望强度。为此，我们提出基于梯度的关系归因解释（G-RAEs），量化主题论点强度对各边权重变化的敏感性，从而提供可解释的权重调整指导。在此基础上，开发了迭代算法，逐步调整边权重以达成目标强度。我们在模拟个性化推荐系统和多层感知机结构特征的合成EW-QBAFs上进行实验，验证了该方法的有效性。
原文摘要 · Abstract (English)
Contestable AI requires that AI-driven decisions align with human preferences. While various forms of argumentation have been shown to support contestability, Edge-Weighted Quantitative Bipolar Argumentation Frameworks (EW-QBAFs) have received little attention. In this work, we show how EW-QBAFs can be deployed for this purpose. Specifically, we introduce the contestability problem for EW-QBAFs, which asks how to modify edge weights (e.g., preferences) to achieve a desired strength for a specific argument of interest (i.e., a topic argument). To address this problem, we propose gradient-based relation attribution explanations (G-RAEs), which quantify the sensitivity of the topic argument's strength to changes in individual edge weights, thus providing interpretable guidance for weight adjustments towards contestability. Building on G-RAEs, we develop an iterative algorithm that progressively adjusts the edge weights to attain the desired strength. We evaluate our approach experimentally on synthetic EW-QBAFs that simulate the structural characteristics of personalised recommender systems and multi-layer perceptrons, and demonstrate that it can solve the problem effectively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。