{"type":"ScholarlyArticle","title":"Cross-community Adapter Learning (CAL) to Understand the Evolving Meanings of Norm Violation","authors":[{"name":"Thiago Freitas dos Santos","slug":"thiago-freitas-dos-santos","given":"Thiago","family":"Freitas dos Santos","affiliation":"Artificial Intelligence Research Institute (IIIA) , CSIC , Barcelona , Catalonia , Spain; Universitat Autònoma de Barcelona , Catalonia , Spain"},{"name":"Stephen Cranefield","slug":"stephen-cranefield","given":"Stephen","family":"Cranefield","affiliation":"University of Otago , Dunedin , New Zealand"},{"name":"Bastin Tony Roy Savarimuthu","slug":"bastin-tony-roy-savarimuthu","given":"Bastin Tony Roy","family":"Savarimuthu","affiliation":"University of Otago , Dunedin , New Zealand"},{"name":"Nardine Osman","slug":"nardine-osman","given":"Nardine","family":"Osman","affiliation":"Artificial Intelligence Research Institute (IIIA) , CSIC , Barcelona , Catalonia , Spain"},{"name":"Marco Schorlemmer","slug":"marco-schorlemmer","given":"Marco","family":"Schorlemmer","affiliation":"Artificial Intelligence Research Institute (IIIA) , CSIC , Barcelona , Catalonia , Spain"}],"abstract":"Cross-community learning incorporates data from different sources to leverage task-specific solutions in a target community. This approach is particularly interesting for low-resource or newly created online communities, where data formalizing interactions between agents (community members) are limited. In such scenarios, a normative system that intends to regulate online interactions faces the challenge of continuously learning the meaning of norm violation as communities' views evolve, either with changes in the understanding of what it means to violate a norm or with the emergence of new violation classes. To address this issue, we propose the Cross-community Adapter Learning (CAL) framework, which combines adapters and transformer-based models to learn the meaning of norm violations expressed as textual sentences. Additionally, we analyze the differences in the meaning of norm violations between communities, using Integrated Gradients (IG) to understand the inner workings of our model and calculate a global relevance score that indicates the relevance of words for violation detection. Results show that cross-community learning enhances CAL's performance while explaining the differences in the meaning of norm-violating behavior based on community members' feedback. We evaluate our proposal in a small set of interaction data from Wikipedia, in which the norm prohibits hate speech.","doi":"10.24963/ijcai.2023/13","url":"https://ijcai.org/proceedings/2023/13","pdf":"https://ijcai.org/proceedings/2023/0013.pdf","markdown":"https://ijcai.org/proceedings/2023/13.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/13","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":"Agent-based and Multi-agent Systems","track":"Main Track","number":13,"pages":{"first":109,"last":117},"keywords":["Agent-based and Multi-agent Systems: MAS: Normative systems","Machine Learning: ML: Incremental learning","AI Ethics, Trust, Fairness: ETF: Explainability and interpretability"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 2022] Rishabh Agrawal, Nirav Ajmeri, and Munindar P Singh. 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