Where the Evidence Lives: Auditing AI Companions'Self-Descriptions
TL;DR
An audit procedure is demonstrated that sets an agent's self-description against its users'judgements and its implementation records, reporting each claim as supported, contradicted, or unresolved, and applies it to Lita, a proactive companion built and deployed for a month with nine colleagues.
Abstract
Companion agents describe themselves: they remember, they understand their users, the relationship has changed them. We argue that such accounts, and the experience ratings that seem to confirm them, are checkable by users only where the evidence is theirs: in the agent's behavior, or in themselves. Where the evidence lives in the machinery, fluent self-description and moderately positive ratings do not establish that the mechanisms behind them ran. We demonstrate an audit procedure that sets an agent's self-description against its users'judgements and its implementation records, reporting each claim as supported, contradicted, or unresolved, and apply it to Lita, a proactive companion we built and deployed for a month with nine colleagues. Participants endorsed stylistic claims, withheld endorsement from relational ones, and rated memory at or above midpoint, while two of three memory layers had never executed their accumulation step. Memory-bearing agents should report what their self-descriptions cannot establish.
