Beyond Technical Transparency: Explainability as a Safeguard Against Manipulative AI*

Abstract

Large Language Models (LLMs) now write with a fluency and persuasiveness that can subtly steer users' choices. When their outputs lack clear and comprehensible explanations, this persuasive power risks undermining human decision-making capacity, raising serious ethical concerns. Current explainable artificial intelligence (XAI) techniques focus primarily on technical transparency for epistemic purposes (how a model works); they are rarely intended to reveal to the user the kind of influence they are subject to. Drawing on the Indifference View of manipulation, we advance a preliminary framework that reconceives explainability as both an epistemic and an ethical imperative. The core idea is based on explanatory metadata: layered annotations that accompany model outputs with four complementary types of explanation-informative, justificatory, causal, and precautionary-which give models the ability to detail the reasons underlying the influence they exert. Doing so shifts the XAI goal from mere transparency to responsible influence. It positions explanations as a safeguard against the manipulative behavior of generative AI systems, laying the groundwork for future methods that measure, audit, and actively constrain ethically problematic influence.

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