Fairness In a Non-Stationary Environment From an Optimal Control Perspective
Fairness In a Non-Stationary Environment From an Optimal Control Perspective
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发表时间：
2023
影响因子：
0.7
通讯作者：
Zhuotong Chen;Qianxiao Li;Zheng Zhang
中科院分区：
文献类型：
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作者：
Zhuotong Chen;Qianxiao Li;Zheng Zhang
据观察，在训练期间涉及代表性不足的人口群体的场景中，最先进的机器学习模型的性能会下降。这个问题已经在数据分布保持不变的监督学习框架内得到了广泛的研究。尽管如此，现实世界的用例经常会遇到部署中的模型引起的分布变化。例如，针对少数群体用户的性能偏差可能会影响客户保留率，从而由于缺乏少数群体用户的输入而导致活跃用户的可用数据出现偏差。这种反馈效应进一步加剧了后续时间步骤中不同人口群体之间的差异。为了缓解这个问题，我们引入了渐近公平性，这是一个旨在在所有人口群体中保持模型持续性能的标准。此外，我们根据现有的进化种群动态文献构建了一个替代保留系统，以近似活跃用户数量分布变化的动态。该系统允许将实现渐近公平性的目标表述为最优控制问题。为了评估所提出方法的有效性，我们设计了一个通用的模拟环境，模拟用户保留和模型性能之间反馈效应的群体动态。当我们将模型部署到该仿真环境时，通过考虑长期规划，最优控制解决方案优于现有的基线方法，表现出卓越的性能。
The performance of state-of-the-art machine learning models is observed to degrade in scenarios involving under-represented demographic populations during training. This issue has been extensively studied within a supervised learning framework where data distribution remains unchanged. Nonetheless, real-world use cases often encounter distribution shifts induced by the models in deployment. For example, performance bias against minority users can affect customer retention rates, thereby skewing available data from active users due to the absence of minority user input. This feedback effect further exacerbates the discrepancy across various demographic groups in subsequent time steps. To mitigate this problem, we introduce asymptotic fairness, a criterion that aims at preserving sustained model performance across all demographic populations. In addition, we construct a surrogate retention system, based on existing literature on evolutionary population dynamics, to approximate the dynamics of distribution shifts on active user counts. This system allows the aim of achieving asymptotic fairness to be formulated as an optimal control problem. To evaluate the effectiveness of the proposed method, we design a generic simulation environment that simulates the population dynamics of the feedback effect between user retention and model performance. When we deploy the models to this simulation environment, by considering long-term planning, the optimal control solution outperforms existing baseline methods, demonstrating superior performance.