2019/07/09 by Travis Mandel, Mandel, Travis, Jahnu Best +11
Computer Science · #Data Stream Mining Techniques #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #Software Engineering Research
paper · pdf · doi:10.48550/arxiv.1907.04446
openalex publication_date 2019/07/09 · openalex created_date 2022/11/14 · openalex updated_date 2026/07/28
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.