Several recent works have studied the societal effects of AI; these include issues such as fairness, robustness, and safety. In many of these objectives, a learner seeks to minimize its worst-case loss over a set of predefined distributions (known as uncertainty sets), with usual examples being perturbed versions of the empirical distribution. In other words, aforementioned problems can be written as min-max problems over these uncertainty sets. In this work, we provide a general framework for studying these problems, which we refer to as Responsible AI (RAI) games. We provide two classes of algorithms for solving these games: (a) game-play based algorithms, and (b) greedy stagewise estimation algorithms. The former class is motivated by online learning and game theory, whereas the latter class is motivated by the classical statistical literature on boosting, and regression. We empirically demonstrate the applicability and competitive performance of our techniques for solving several RAI problems, particularly around subpopulation shift.

通过对于不确定性集合的极小-极大问题进行研究，我们提供了一个被称为负责任人工智能（RAI）游戏的通用框架，并给出了两类算法来解决这些问题：基于游戏的算法和贪婪分阶段估计算法。我们通过实证方法证明了我们的技术在解决几个RAI问题，特别是在子群体转变方面的适用性和竞争性表现。

负责任的人工智能（RAI）游戏与集合