The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society. In this paper, we develop the concept of ML governance to balance such benefits and risks, with the aim of achieving responsible applications of ML. Our approach first systematizes research towards ascertaining ownership of data and models, thus fostering a notion of identity specific to ML systems. Building on this foundation, we use identities to hold principals accountable for failures of ML systems through both attribution and auditing. To increase trust in ML systems, we then survey techniques for developing assurance, i.e., confidence that the system meets its security requirements and does not exhibit certain known failures. This leads us to highlight the need for techniques that allow a model owner to manage the life cycle of their system, e.g., to patch or retire their ML system. Put altogether, our systematization of knowledge standardizes the interactions between principals involved in the deployment of ML throughout its life cycle. We highlight opportunities for future work, e.g., to formalize the resulting game between ML principals.
翻译:在计算机系统中应用机器学习(ML)不仅给社会带来许多好处,也带来风险。在本文件中,我们发展ML治理概念,以平衡这些好处和风险,目的是实现负责任地应用ML。我们的方法首先系统化研究,以确定数据和模型的所有权,从而形成一个针对ML系统的身份概念。在此基础上,我们利用身份来对通过归属和审计导致ML系统失败的负责人负责。为了增加对ML系统的信任,我们然后调查发展保证的技巧,即相信该系统符合其安全要求,而不会出现某些已知的失败。这导致我们强调,有必要采用一些技术,使模型所有人能够管理其系统的生命周期,例如修补或更新其ML系统。简而言之,我们的知识系统化使参与ML整个生命周期部署的负责人之间的互动标准化。我们强调未来工作的机会,例如将ML主之间的游戏正规化。