解释驱动的主动特征获取用于算法补救

中文摘要 AI 辅助

算法补救方法通常假设预测模型可以访问个体的所有特征。在实践中，决策往往是在信息不完整的情况下做出的，因为获取特征的成本很高。主动特征获取解决了成本受限的预测问题，但现有方法对解释不敏感：先前的工作仅在获取额外特征后提供解释，而非利用解释来驱动特征获取。本研究颠覆了这一思路，将算法补救与特征获取联合处理。我们利用马尔可夫毯理论统一了反事实、半事实和替代事实解释，并刻画了随着特征获取而增长的可用补救范围。在此框架基础上，我们提出了一种解释驱动的特征获取（EDFA）方法，该方法根据每单位成本的解释价值来选择特征。该框架进一步扩展了基于部分信息发布的补救的分布无关有效性保证，这些保证标志着可信赖、低成本的补救，以及认证这些保证所需的校准数据的下界。在7个公开数据集上使用基于神经网络的预测模型进行的实验表明，EDFA比最先进的AFA基线获取的特征少得多，同时保持相当的准确性，并产生更多与决策相关、可操作的补救。该实现可在GitHub上获取。

英文摘要

Algorithmic recourse methods typically assume that a predictive model has access to all features of an individual. In practice, decisions are often made with partial information, because features are costly to acquire. Active feature acquisition addresses cost-constrained prediction, but existing methods are explanation-agnostic: prior work provides explanations only after acquiring additional features, rather than using explanations to drive acquisition. This work flips that and treats algorithmic recourse and feature acquisition jointly. We use Markov Blanket theory to unify counterfactual, semifactual, and alterfactual explanations and to characterize how available recourse grows as features are acquired. Building on this framework, we propose an Explanation-Driven Feature Acquisition (EDFA) method that selects features by explanatory value per unit cost. The framework is further extended with distribution-free validity guarantees for recourse issued from partial information, which signal trustworthy, lower-cost recourse, along with a lower bound on the calibration data required to certify them. Experiments on 7 publicly available datasets with neural network-based predictive models show that EDFA acquires substantially fewer features than state-of-the-art AFA baselines while maintaining comparable accuracy and yielding more decision-relevant, actionable recourse. The implementation is available on GitHub.

发表机构

• Western University（西安大略大学）

机构由 AI 辅助整理，请以论文原文为准。