{"type":"ScholarlyArticle","title":"Argumentation for Interactive Causal Discovery","authors":[{"name":"Fabrizio Russo","slug":"fabrizio-russo","given":"Fabrizio","family":"Russo","affiliation":"Imperial College London"}],"abstract":"Causal reasoning reflects how humans perceive events in the world and establish relationships among them, identifying some as causes and others as effects. Causal discovery is about agreeing on these relationships and drawing them as a causal graph.\r\nArgumentation is the way humans reason systematically about an idea: the medium we use to exchange opinions, to get to know and trust each other and possibly agree on controversial matters. \r\nDeveloping AI which can argue with humans about causality would allow us to understand and validate the analysis of the AI and would allow the AI to bring evidence for or against humans' prior knowledge.\r\nThis is the goal of this project: to develop a novel scientific paradigm of interactive causal discovery and train AI to recognise causes and effects by debating, with humans, the results of different statistical methods","doi":"10.24963/ijcai.2023/820","url":"https://ijcai.org/proceedings/2023/820","pdf":"https://ijcai.org/proceedings/2023/0820.pdf","markdown":"https://ijcai.org/proceedings/2023/820.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/820","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":"Doctoral Consortium","track":"Doctoral Consortium","number":820,"pages":{"first":7091,"last":7092},"keywords":["Knowledge Representation and Reasoning: KRR: Argumentation","Machine Learning: ML: Explainable/Interpretable machine learning","Knowledge Representation and Reasoning: KRR: Causality","Humans and AI: HAI: Human-AI collaboration","Machine Learning: ML: Knowledge-aided learning","Uncertainty in AI: UAI: Causality, structural causal models and causal inference","Machine Learning: ML: Causality","Uncertainty in AI: UAI: Graphical models","AI Ethics, Trust, Fairness: ETF: Trustworthy AI"],"videos":[],"references":[{"position":1,"raw":"[Alrajeh et al., 2020] Dalal Alrajeh, Hana Chockler, and Joseph Y Halpern. 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