可信机器学习的监管博弈
Regulation Games for Trustworthy Machine Learning
AI总结：
提出将可信机器学习建模为多智能体博弈的监管博弈框架，并引入ParetoPlay均衡搜索算法，确保智能体在帕累托前沿上，为ML监管提供政策指导，如性别分类中可降低差分隐私预算。
摘要：
现有的可信机器学习（ML）研究往往集中于信任的个别方面，如公平性或隐私性。此外，许多技术忽视了训练机器学习模型的人与负责评估其可信度的人之间的区别。为解决这些问题，我们提出了一个将可信机器学习视为多目标多智能体优化问题的框架。这自然引出了一个博弈论公式，我们称之为监管博弈。我们阐述了一个特定的博弈实例，即SpecGame，在其中我们建模了机器学习模型构建者与公平性和隐私监管者之间的关系。监管者希望设计惩罚措施以强制遵守其规范，但不想阻止构建者参与。为了寻求这种社会最优（即对所有智能体都高效）的博弈解，我们引入了ParetoPlay。这种新颖的均衡搜索算法确保智能体保持在其目标的帕累托前沿上，并避免了其他均衡的低效率。通过ParetoPlay模拟SpecGame可以为机器学习监管提供政策指导。例如，我们表明，在性别分类应用中，如果监管者主动首先指定其期望的保证，他们可以强制执行平均低4.0的差分隐私预算。
英文摘要：
Existing work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy. Additionally, many techniques overlook the distinction between those who train ML models and those responsible for assessing their trustworthiness. To address these issues, we propose a framework that views trustworthy ML as a multi-objective multi-agent optimization problem. This naturally lends itself to a game-theoretic formulation we call regulation games. We illustrate a particular game instance, the SpecGame in which we model the relationship between an ML model builder and fairness and privacy regulators. Regulators wish to design penalties that enforce compliance with their specification, but do not want to discourage builders from participation. Seeking such socially optimal (i.e., efficient for all agents) solutions to the game, we introduce ParetoPlay. This novel equilibrium search algorithm ensures that agents remain on the Pareto frontier of their objectives and avoids the inefficiencies of other equilibria. Simulating SpecGame through ParetoPlay can provide policy guidance for ML Regulation. For instance, we show that for a gender classification application, regulators can enforce a differential privacy budget that is on average 4.0 lower if they take the initiative to specify their desired guarantee first.
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