The article explores the emerging domain of incentive-aware machine learning (ML), which focuses on algorithmic decision-making in contexts where individuals can strategically modify their inputs to influence outcomes. It categorizes the research into three perspectives: robustness, aiming to design models resilient to "gaming"; fairness, analyzing the societal impacts of such systems; and improvement/causality, recognizing situations where strategic actions lead to genuine personal or societal improvement. The paper introduces a unified framework encapsulating models for these perspectives, including offline, online, and causal settings, and highlights key challenges such as differentiating between gaming and improvement and addressing heterogeneity among agents. By synthesizing findings from diverse works, we outline theoretical advancements and practical solutions for robust, fair, and causally-informed incentive-aware ML systems.

本研究解决了激励意识机器学习领域中的一项挑战，即在个体可以通过策略性修改输入来影响算法决策的情况下如何设计稳健和公平的模型。文章提出了一种统一框架，涵盖多个视角，包括稳健性、公平性及因果性，强调了区分游戏化与实际改善之间的关键挑战，并为实现激励意识的机器学习系统提供了理论和实践上的进展。

激励意识机器学习：稳健性、公平性、改善与因果关系