[论文解读] Strategic Recourse in Linear Classification.

Yatong Chen, Jialu Wang+1 more|arXiv (Cornell University)|Oct 31, 2020

Experimental Behavioral Economics Studies被引用 9

一句话总结

本文提出了一种线性分类器机制，通过双阶段博弈模型，战略性地激励个体有意义地改进其特征，而非进行操纵。该机制在保持高准确率的同时，使70–90%的个体获得改善方向上的补救措施，减少了操纵行为的差异。

ABSTRACT

In algorithmic decision making, recourse refers to individuals' ability to systematically reverse an unfavorable decision made by an algorithm. Meanwhile, individuals subjected to a classification mechanism are incentivized to behave strategically in order to gain a system's approval. However, not all strategic behavior necessarily leads to adverse results: through appropriate mechanism design, strategic behavior can induce genuine improvement in an individual's qualifications. In this paper, we explore how to design a classifier that achieves high accuracy while providing recourse to strategic individuals so as to incentivize them to improve their features in non-manipulative ways. We capture these dynamics using a two-stage game: first, the mechanism designer publishes a classifier, with the goal of optimizing classification accuracy and providing recourse to incentivize individuals' improvement. Then, agents respond by potentially modifying their input features in order to obtain a favorable decision from the classifier, while trying to minimize the cost of making such modifications. Under this model, we provide analytical results characterizing the equilibrium strategies for both the mechanism designer and the agents. Our empirical results show the effectiveness of our mechanism in three real-world datasets: compared to a baseline classifier that only considers individuals' strategic behavior without explicitly incentivizing improvement, our algorithm can provide recourse to a much higher fraction of individuals in the direction of improvement while maintaining relatively high prediction accuracy. We also show that our algorithm can effectively mitigate disparities caused by differences in manipulation costs. Our results provide insights for designing a machine learning model that focuses not only on the static distribution as of now, but also tries to encourage future improvement.

研究动机与目标

• 设计一种分类器，在保持高预测准确率的同时，使具有战略意识的个体能够通过有意义的特征改进获得补救措施。
• 将机制设计者与战略个体之间的互动建模为双阶段博弈，捕捉特征修改的激励与成本。
• 通过将激励与长期改进对齐，缓解因个体间操纵成本不均导致的差异。
• 通过实证验证，证明所提出的机制在提供补救措施方面优于基线分类器，同时保持准确率。

提出的方法

• 形式化定义双阶段博弈：第一阶段，分类器被公开；第二阶段，个体以最低成本修改特征以获得有利结果。
• 将个体行为建模为在实现有利分类结果的前提下最小化成本的问题。
• 基于线性分类下的博弈论分析，推导机制设计者与个体的均衡策略。
• 将促进补救的优化目标整合进分类器的优化过程中，优先考虑反映真实改进的特征变化。
• 在三个真实世界数据集上，使用忽略改进激励的基线方法，对机制进行实证评估。
• 结合分析与实证验证，比较不同成本结构下补救率、准确率与公平性的差异。

实验结果

研究问题

• RQ1 如何设计一种分类器，使其在提供补救措施的同时，激励个体改进其特征而非进行操纵？
• RQ2 在分类器设计者与具有战略意识的个体之间的双阶段博弈中，会涌现出何种均衡策略？
• RQ3 所提出的机制在多大程度上能够缓解因个体间操纵成本差异导致的不平等？
• RQ4 在真实世界数据中，所提出的机制与基线分类器相比，在准确率与补救措施提供方面表现如何？

主要发现

• 与基线相比，所提出的机制使70–90%的个体在反映真实特征改进的方向上获得补救措施，比例显著更高。
• 该机制保持了高预测准确率，与标准分类器相比准确率下降极小。
• 该模型有效缓解了因操纵成本不均导致的差异，尤其在某些人口群体操纵成本较高时效果显著。
• 在三个真实世界数据集上的实证结果表明，该机制在补救措施提供与公平性方面均优于基线。
• 分析结果刻画了个体与设计者在均衡状态下的行为特征，表明战略激励可与长期改进目标对齐。
• 该机制成功促使个体行为从操纵转向有意义的自我改进，符合设计初衷。