论文标题
偏见的算法框架
An Algorithmic Framework for Bias Bounties
论文作者
论文摘要
我们建议和分析一个“偏见赏金”的算法框架：邀请外部参与者提出改进经过训练的模型的事件，类似于软件和安全性中的错误赏金事件。我们的框架允许参与者提交任意子组改进，然后将其算法合并到更新的模型中。我们的算法具有整体和亚组精度之间或不同子组精度之间没有张力的属性，并且它享有与贝叶斯最佳模型的可证明的融合或参与者无法进一步改进的状态。我们从初步偏见赏金事件中提供了对框架，实验评估和发现的正式分析。
We propose and analyze an algorithmic framework for "bias bounties": events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security. Our framework allows participants to submit arbitrary subgroup improvements, which are then algorithmically incorporated into an updated model. Our algorithm has the property that there is no tension between overall and subgroup accuracies, nor between different subgroup accuracies, and it enjoys provable convergence to either the Bayes optimal model or a state in which no further improvements can be found by the participants. We provide formal analyses of our framework, experimental evaluation, and findings from a preliminary bias bounty event.
