算法管理对零工劳动者的影响非线性，透明规则能提升绩效与福祉。
When Algorithms Manage Humans: A Double Machine Learning Approach to Estimating Nonlinear Effects of Algorithmic Control on Gig Worker Performance and Wellbeing
- 用双重机器学习方法分析算法控制的非线性影响，不预设函数形式。
- 中等程度的算法监督导致福祉提升但绩效下降，透明时效果逆转。
- 适合关注平台治理、组织行为学或算法公平性的研究者阅读。
未来工作的一个核心问题是：当算法承担管理角色时，以人为本的管理能否持续？传统工具常因忽略劳动者对算法系统的非线性响应而失效。本文采用双重机器学习框架，估计无需强假设的调节中介模型。基于464名零工劳动者调查数据，发现清晰的非单调模式：支持性人力资源实践可提升福祉，但在算法监管存在但难以理解的中间地带，其对绩效的促进作用减弱；当监管透明且可解释时，该关系重新增强。结果表明，简单线性模型可能遗漏真实模式，甚至得出相反结论。对平台设计而言，启示明确：部分定义的控制引发困惑，但清晰规则与可信申诉机制能使高强度监管可行。方法上，本文展示双重机器学习如何在组织研究中无须强制数据线性化，即估计条件间接效应。
原文摘要 · Abstract (English)
A central question for the future of work is whether person centered management can survive when algorithms take on managerial roles. Standard tools often miss what is happening because worker responses to algorithmic systems are rarely linear. We use a Double Machine Learning framework to estimate a moderated mediation model without imposing restrictive functional forms. Using survey data from 464 gig workers, we find a clear nonmonotonic pattern. Supportive HR practices improve worker wellbeing, but their link to performance weakens in a murky middle where algorithmic oversight is present yet hard to interpret. The relationship strengthens again when oversight is transparent and explainable. These results show why simple linear specifications can miss the pattern and sometimes suggest the opposite conclusion. For platform design, the message is practical: control that is only partly defined creates confusion, but clear rules and credible recourse can make strong oversight workable. Methodologically, the paper shows how Double Machine Learning can be used to estimate conditional indirect effects in organizational research without forcing the data into a linear shape.
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