TY - JOUR
T1 - Causation And Argumentation
AU - Bochman, Alexander
AU - Cerutti, Federico
AU - Rienstra, Tjitze
N1 - Funding Information:
This work was supported by the EU NEXTGENERATIONEU program within the PNRR Future Artificial Intelligence \u2013 FAIR project (PE0000013, CUP H23C22000860006), Objective 10: Abstract Argumentation for Knowledge Representation and Reasoning, specifically by the project Argumentation for Informed Decisions with Applications to Energy Consumption in Computing \u2013 AIDECC (CUP D53C24000530001). This work was supported by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union \u2013 NextGenerationEU, specifically by the project NEACD: Neurosymbolic Enhanced Active Cyber Defence (CUP J33C22002810001). This work was supported by project ACRE (AI-Based Causality and Reasoning for Deceptive Assets-2022EP2L7H) and xInternet (eXplainable Internet-20225CETN9) projects-funded by European Union-Next Generation EU within the PRIN 2022 program (D.D. 104-02/02/2022 Ministero dell\u2019Universit\u00E0 e della Ricerca). The work is partially supported by the European Office of Aerospace Research & Development and the Air Force Office of Scientific Research under award number FA8655-22-1-7017 and by the US DEVCOM Army Research Laboratory (ARL) under Cooperative Agreement #W911NF2220243. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States government.
Publisher Copyright:
© 2025, College Publications. All rights reserved.
PY - 2025/3/1
Y1 - 2025/3/1
N2 - Causality is a feature in a socio-economical context rapidly moving towards an ethical use of robust artificial intelligence. The primary link between causation and argumentation, especially in AI, stems from the fundamental role of causality in explanations, as argued in several works in the explainable artificial intelligence literature. In this sense, theories of causation naturally sug-gest themselves as an essential component of explainable artificial intelligence. Causality also directly supports what-if and counterfactual reasoning, fundamental components for fair, robust, and resilient use of artificial intelligence tools and systems. Because of its connection with the enquiry, persuasion, and negotiation monologues and dialogues, this article popularizes the fundamental concepts of causality for the computational argumentation research community. It also accounts for the approaches to address research questions at the heart of both argumentation and causality communities, including the connections between causal models and formal argumentation approaches.
AB - Causality is a feature in a socio-economical context rapidly moving towards an ethical use of robust artificial intelligence. The primary link between causation and argumentation, especially in AI, stems from the fundamental role of causality in explanations, as argued in several works in the explainable artificial intelligence literature. In this sense, theories of causation naturally sug-gest themselves as an essential component of explainable artificial intelligence. Causality also directly supports what-if and counterfactual reasoning, fundamental components for fair, robust, and resilient use of artificial intelligence tools and systems. Because of its connection with the enquiry, persuasion, and negotiation monologues and dialogues, this article popularizes the fundamental concepts of causality for the computational argumentation research community. It also accounts for the approaches to address research questions at the heart of both argumentation and causality communities, including the connections between causal models and formal argumentation approaches.
M3 - Article
SN - 2631-9810
VL - 12
SP - 713
EP - 786
JO - Journal of Applied Logics
JF - Journal of Applied Logics
IS - 3
ER -