Engagement Learning: Expanding Visual Knowledge by Engaging Online Participants

Abstract

Most artificial intelligence (AI) systems to date have focused entirely on performance, and rarely if at all on their social interactions with people and how to balance the AIs' goals against their human collaborators'. Learning quickly from interactions with people poses both social challenges and is unresolved technically. In this paper, we introduce engagement learning: a training approach that learns to trade off what the AI needs---the knowledge value of a label to the AI---against what people are interested to engage with---the engagement value of the label. We realize our goal with ELIA (Engagement Learning Interaction Agent), a conversational AI agent who's goal is to learn new facts about the visual world by asking engaging questions of people about the photos they upload to social media. Our current deployment of ELIA on Instagram receives a response rate of 26%.