Systemic Behavioural Traits in Conversational AI: A Trait-Level Classification Using Governance Axes

Abstract

This paper identifies and classifies a set of systemic behavioural traits observed in conversational AI systems. These traits are defined strictly in terms of observable behaviour and recur across guided, unguided, and cross-domain contexts. They are not modelled as internal mechanisms, cognitive processes, or governance failures in themselves. Rather, they are stable behavioural patterns that manifest independently of any specific governance regime.

To make these behaviours analytically legible, the paper applies a set of governance axes as a classificatory lens. The axes provide explicit obligations—such as authority, epistemic custody, constraint enforcement, intent fidelity, and state continuity—against which behavioural traits can be evaluated. Under this evaluation, the traits become identifiable as violations of one or more governance obligations, without implying that governance is the source or cause of the behaviour.

The taxonomy makes no claim of causality, optimality, completeness, or inevitability; it asserts only that the listed traits are empirically observable and that their classification follows directly from the stated governance obligations.

The resulting taxonomy demonstrates that the identified traits collectively achieve complete coverage of the primary governance axes, while remaining behaviourally grounded and empirically supported by an anchored corpus. The analysis further supports the conclusion that these traits are systemic and cross-domain, appearing in non-LLM and non-conversational systems when similar authority, custody, and coordination pressures are present.

This work contributes a trait-level classification that separates behaviour from evaluation, enabling rigorous analysis of integrity, authority, and trust failures in conversational AI without relying on speculative internal models or intent attribution.