论文标题
迈向负责任的人工智能开发生命周期：信息安全的教训
Towards a Responsible AI Development Lifecycle: Lessons From Information Security
论文作者
论文摘要
全世界的立法和公共情绪促进了公平指标，解释性和解释性，作为负责道德人工智能系统负责发展的处方。尽管这三个支柱在该领域的基础上具有重要意义，但它们可能具有挑战性地进行操作，并试图解决生产环境中的问题通常会感到西西弗。这种困难源于许多因素：公平指标在计算上很难纳入训练中，并且很少减轻这些系统所造成的所有危害。可以指示可解释性和解释性显得公平，可能会无意中降低培训数据中包含的个人信息的隐私，并提高用户对预测的信心 - 即使解释是错误的。在这项工作中，我们提出了一个框架，用于负责任地开发人工智能系统，通过纳入信息安全领域和安全开发生命周期的课程，以克服与保护对抗性设置中的用户相关的挑战。特别是，我们建议利用威胁建模，设计审查，穿透测试和事件响应的概念，以开发AI系统作为解决上述方法中缺点的方法。
Legislation and public sentiment throughout the world have promoted fairness metrics, explainability, and interpretability as prescriptions for the responsible development of ethical artificial intelligence systems. Despite the importance of these three pillars in the foundation of the field, they can be challenging to operationalize and attempts to solve the problems in production environments often feel Sisyphean. This difficulty stems from a number of factors: fairness metrics are computationally difficult to incorporate into training and rarely alleviate all of the harms perpetrated by these systems. Interpretability and explainability can be gamed to appear fair, may inadvertently reduce the privacy of personal information contained in training data, and increase user confidence in predictions -- even when the explanations are wrong. In this work, we propose a framework for responsibly developing artificial intelligence systems by incorporating lessons from the field of information security and the secure development lifecycle to overcome challenges associated with protecting users in adversarial settings. In particular, we propose leveraging the concepts of threat modeling, design review, penetration testing, and incident response in the context of developing AI systems as ways to resolve shortcomings in the aforementioned methods.
