让我们保持安全：设计用户界面，让每个人都能为AI安全做出贡献
Let's Keep It Safe: Designing User Interfaces that Allow Everyone to Contribute to AI Safety
摘要：
当人工智能系统被授权在现实世界中采取有影响力的行动时，这些系统的行为方式存在固有的风险。通常情况下，人类会对AI系统指定约束以防止有害行为;然而，很少有工作研究如何最好地促进这一困难的约束规范过程。在本文中，我们研究如何设计用户界面，使这一过程更有效和更容易，让人们与不同的背景和专业知识水平，以促进这一任务。我们首先提出了一个任务设计，工人评估个人的状态-动作对的安全性，并提出了几个变种，这个任务与改进的任务设计和过滤机制。虽然第一个设计很容易理解，但它很难扩展到大的状态空间。因此，我们开发了一个新的用户界面，允许工人编写约束规则，而无需任何编程。尽管它的简单性，我们表明，我们的规则建设接口保留充分的表现力。我们提出了利用众包工作者来帮助解决教育领域中一个重要的现实世界AI安全问题的实验。我们的研究结果表明，我们新的工人过滤和解释方法优于基线方法，我们的基于规则的界面，使工人更有效率，同时提高数据质量。
英文摘要：
When AI systems are granted the agency to take impactful actions in the real world, there is an inherent risk that these systems behave in ways that are harmful. Typically, humans specify constraints on the AI system to prevent harmful behavior; however, very little work has studied how best to facilitate this difficult constraint specification process. In this paper, we study how to design user interfaces that make this process more effective and accessible, allowing people with a diversity of backgrounds and levels of expertise to contribute to this task. We first present a task design in which workers evaluate the safety of individual state-action pairs, and propose several variants of this task with improved task design and filtering mechanisms. Although this first design is easy to understand, it scales poorly to large state spaces. Therefore, we develop a new user interface that allows workers to write constraint rules without any programming. Despite its simplicity, we show that our rule construction interface retains full expressiveness. We present experiments utilizing crowdworkers to help address an important real-world AI safety problem in the domain of education. Our results indicate that our novel worker filtering and explanation methods outperform baseline approaches, and our rule-based interface allows workers to be much more efficient while improving data quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
