As machine learning (ML) models are increasingly used in social domains to make consequential decisions about humans, they often have the power to reshape data distributions. Humans, as strategic agents, continuously adapt their behaviors in response to the learning system. As populations change dynamically, ML systems may need frequent updates to ensure high performance. However, acquiring high-quality human-annotated samples can be highly challenging and even infeasible in social domains. A common practice to address this issue is using the model itself to annotate unlabeled data samples. This paper investigates the long-term impacts when ML models are retrained with model-annotated samples when they incorporate human strategic responses. We first formalize the interactions between strategic agents and the model and then analyze how they evolve under such dynamic interactions. We find that agents are increasingly likely to receive positive decisions as the model gets retrained, whereas the proportion of agents with positive labels may decrease over time. We thus propose a refined retraining process to stabilize the dynamics. Last, we examine how algorithmic fairness can be affected by these retraining processes and find that enforcing common fairness constraints at every round may not benefit the disadvantaged group in the long run. Experiments on (semi-)synthetic and real data validate the theoretical findings.

机器学习模型在社会领域中应用越来越广泛，本文研究了当模型利用自身注释的样本进行重训练，并结合人类战略反应时的长期影响，分析了它们在动态交互下的演变，并提出了一种改进的重训练过程来稳定这种动态，最后研究了这些重训练过程如何影响算法公平性，发现在每个回合都强制执行公平性约束可能无法长期惠及弱势群体。

自动化数据标注在战略人工智能代理下的风险和潜在解决方案