{"type":"ScholarlyArticle","title":"On Optimizing Model Generality in AI-based Disaster Damage Assessment: A Subjective Logic-driven Crowd-AI Hybrid Learning Approach","authors":[{"name":"Yang Zhang","slug":"yang-zhang","given":"Yang","family":"Zhang","affiliation":"University of Illinois Urbana-Champaign {yzhangnd , rzong2 , lshang3 ,"},{"name":"Ruohan Zong","slug":"ruohan-zong","given":"Ruohan","family":"Zong","affiliation":"University of Illinois Urbana-Champaign {yzhangnd , rzong2 , lshang3 ,"},{"name":"Lanyu Shang","slug":"lanyu-shang","given":"Lanyu","family":"Shang","affiliation":"University of Illinois Urbana-Champaign {yzhangnd , rzong2 , lshang3 ,"},{"name":"Huimin Zeng","slug":"huimin-zeng","given":"Huimin","family":"Zeng","affiliation":"University of Illinois Urbana-Champaign {yzhangnd , rzong2 , lshang3 ,"},{"name":"Zhenrui Yue","slug":"zhenrui-yue","given":"Zhenrui","family":"Yue","affiliation":"University of Illinois Urbana-Champaign {yzhangnd , rzong2 , lshang3 ,"},{"name":"Na Wei","slug":"na-wei","given":"Na","family":"Wei","affiliation":"University of Illinois Urbana-Champaign {yzhangnd , rzong2 , lshang3 ,"},{"name":"Dong Wang","slug":"dong-wang","given":"Dong","family":"Wang","affiliation":"University of Illinois Urbana-Champaign {yzhangnd , rzong2 , lshang3 ,"}],"abstract":"This paper focuses on the AI-based damage assessment (ADA) applications that leverage state-of-the-art AI techniques to automatically assess the disaster damage severity using online social media imagery data, which aligns well with the ''disaster risk reduction'' target under United Nations' Sustainable Development Goals (UN SDGs). This paper studies an ADA model generality problem where the objective is to address the limitation of current ADA solutions that are often optimized only for a single disaster event and lack the generality to provide accurate performance across different disaster events. To address this limitation, we work with domain experts and local community stakeholders in disaster response to develop CollabGeneral, a subjective logic-driven crowd-AI collaborative learning framework that integrates AI and crowdsourced human intelligence into a principled learning framework to address the ADA model generality problem. Extensive experiments on four real-world ADA datasets demonstrate that CollabGeneral consistently outperforms the state-of-the-art baselines by significantly improving the ADA model generality across different disasters.","doi":"10.24963/ijcai.2023/701","url":"https://ijcai.org/proceedings/2023/701","pdf":"https://ijcai.org/proceedings/2023/0701.pdf","markdown":"https://ijcai.org/proceedings/2023/701.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/701","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":701,"pages":{"first":6317,"last":6325},"keywords":["AI for Good: Multidisciplinary Topics and Applications","AI for Good: Humans and AI"],"videos":[],"references":[{"position":1,"raw":"[Barmpoutis et al., 2019] Panagiotis Barmpoutis, Kosmas Dimitropoulos, Kyriaki Kaza, and Nikos Grammalidis. 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