Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us

Computer Science > Artificial Intelligence [Submitted on 16 Mar 2026] Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us Stylianos Loukas Vasileiou, Antonio Rago, Francesca Toni, William Yeoh Computational argumentation offers formal frameworks for transparent, verifiable reasoning but has traditionally been limited by its reliance on domain-specific information and extensive feature engineering. In contrast, LLMs excel at processing unstructured text, yet their opaque nature makes their reasoning difficult to evaluate and trust. We argue that the convergence of these fields will lay the foundation for a new paradigm: Argumentative Human-AI Decision-Making. We analyze how the synergy of argumentation framework mining, argumentation framework synthesis, and argumentative reasoning enables agents that do not just justify decisions, but engage in dialectical processes where decisions are contestable and revisable -- reasoning with humans rather than for them. This convergence of computational argumentation and LLMs is essential for human-aware, trustworthy AI in high-stakes domains. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.15946 [cs.AI]   (or arXiv:2603.15946v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.15946 Focus to learn more Submission history From: Stylianos Loukas Vasileiou [view email] [v1] Mon, 16 Mar 2026 21:51:36 UTC (72 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)