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Framing and Priming in Dialogue Systems: New Forms of Speech Manipulation Through AI

Authors: Habibova, Konul Azizaga;

Framing and Priming in Dialogue Systems: New Forms of Speech Manipulation Through AI

Abstract

В статье исследуются когнитивные механизмы фрейминга (языкового обрамления) и прайминга (предваряющего внушения) в контексте взаимодействия человека с диалоговыми системами на основе искусственного интеллекта (ИИ). Автор предлагает междисциплинарный анализ, объединяющий когнитивную лингвистику, психолингвистику, социальную психологию и технологии генеративного ИИ, с целью выявить способы, посредством которых такие системы могут использовать речевые стратегии убеждения и манипуляции. Показано, что диалоговые ИИ активно применяют фреймы при подаче информации – как в лексическом оформлении, так и в тональности высказываний, – что может искажать интерпретации и направлять мнение пользователя. Параллельно прайминг позволяет системам формировать установки ещё до основной реплики – через тон, порядок подачи данных или даже фоновую информацию, что особенно эффективно в длительном взаимодействии. Автор обсуждает угрозы автономии пользователя, особенно уязвимых групп (дети, пожилые, лица с ментальными особенностями), проблемы обмана через антропоморфизацию ИИ и подмены реального диалога симулированной «дружбой». Рассматриваются юридические и нормативные инициативы, включая положения Европейского Акта об ИИ (2024), запрещающие использование ИИ для скрытого поведенческого влияния. Предлагаются также возможные решения – от маркировки ИИ-контента и прозрачности алгоритмов до повышения цифровой грамотности пользователей. Таким образом, статья вносит вклад в понимание речевых механизмов, задействованных в коммуникации человек–ИИ, и подчёркивает необходимость баланса между технологическим развитием и сохранением когнитивной свободы человека.

The article explores the cognitive mechanisms of framing (language framing) and priming (anticipatory suggestion) in the context of human interaction with artificial intelligence (AI)-based dialogue systems. The author proposes an interdisciplinary analysis combining cognitive linguistics, psycholinguistics, social psychology and generative AI technologies to identify how such systems can use speech strategies of persuasion and manipulation. It is shown that dialogue AIs actively apply frames in the presentation of information, both in lexical framing and in the tone of utterances, which can skew interpretations and guide the user's opinion. In parallel, priming allows systems to shape attitudes even before the main utterance, through tone, order of presentation, or even background information, which is particularly effective in long-term interactions. The author discusses threats to user autonomy, especially for vulnerable groups (children, the elderly, people with mental disabilities), the problems of deception through anthropomorphisation of AI and substitution of real dialogue with simulated 'friendship'. Legal and regulatory initiatives, including the European AI Act (2024) provisions, prohibit AI use for covert behavioural influence and are considered. Possible solutions are also suggested, ranging from AI content labelling and algorithm transparency to increasing digital literacy among users. Thus, the article contributes to understanding the speech mechanisms involved in human-AI communication and emphasises the need for a balance between technological development and the preservation of human cognitive freedom.

Keywords

LСC Subject Category: PE1001-1693, Languages, speech manipulation; AI; framing; priming; cognitive linguistics; verbal influence, речевая манипуляция; ИИ; фрейминг; прайминг; когнитивная лингвистика; речевое воздействие

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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