  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.
