CollabEquality: A Crowd-AI Collaborative Learning Framework to Address Class-wise Inequality in Web-based Disaster Response

Web-based disaster response (WebDR) is emerging as a pervasive approach to acquire real-time situation awareness of disaster events by collecting timely observations from the Web (e.g., social media). This paper studies a class-wise inequality problem in WebDR applications where the objective is to address the limitation of current WebDR solutions that often have imbalanced classification performance across different classes. To address such a limitation, this paper explores the collaborative strengths of the diversified yet complementary biases of AI and crowdsourced human intelligence to ensure a more balanced and accurate performance for WebDR applications. However, two critical challenges exist: 1) it is difficult to identify the imbalanced AI results without knowing the ground-truth WebDR labels a priori; ii) it is non-trivial to address the class-wise inequality problem using potentially imperfect crowd labels. To address the above challenges, we develop CollabEquality, an inequality-aware crowd-AI collaborative learning framework that carefully models the inequality bias of both AI and human intelligence from crowdsourcing systems into a principled learning framework. Extensive experiments on two real-world WebDR applications demonstrate that CollabEquality consistently outperforms the state-of-the-art baselines by significantly reducing class-wise inequality while improving the WebDR classification accuracy.