Argumentumai

Argumentumai is a term used to describe argumentative outputs produced by artificial intelligence systems. It appears in discussions of computational rhetoric and AI-assisted argumentation to distinguish machine-generated arguments from those created by humans. The term is a concise coinage blending argumentum, Latin for argument, with AI, and is used as a descriptive label rather than a formal theory.

Scope and characteristics. Argumentumai can take written form, spoken dialogue, or interactive exchanges. They typically present premises, evidence, a conclusion, and may employ rhetorical devices intended to persuade. The quality of argumentumai varies widely, from tightly reasoned, logically coherent arguments to superficially plausible patterns learned from training data. Key concerns include transparency about premise sourcing, potential bias, and the risk of manipulation through persuasive design.

Evaluation and critique. Researchers study argumentumai for logical validity and soundness, relevance of premises, coherence, and rhetorical effectiveness. They also consider ethical implications, provenance, and accountability. Methods include formal argument analysis, qualitative assessment, and user studies, as well as techniques to detect AI authorship and to audit model behavior.

Applications and limitations. In education, argumentumai can aid students in analyzing arguments or practicing debate. In journalism, policy analysis, and content moderation, they can support rapid synthesis and testing of positions. Limitations include dependence on training data, occasional generation of hallucinations or errors, bias, and the challenge of explainability. Ongoing governance and policy debates address responsibility for generated content and safeguards against manipulation.