2026· Bakhtiniana: Revista de Estudos do Discurso· 0 citations
TL;DR
The ethical limits of AI, particularly generative AI, such as ChatGPT, are discussed, questioning its impact on human processes of interaction and meaning-making, and the extent to which AI can engage in authentic dialogue is assessed.
Abstract
ABSTRACT In this article, we aim to discuss, based on Bakhtinian dialogic theory, the ethical limits of AI, particularly generative AI, such as ChatGPT, questioning its impact on human processes of interaction and meaning-making. To this end, we present a historical overview of the origins of AI as a process embedded in transformations in social relations. Next, grounded in Bakhtinian theoretical principles of dialogue, responsiveness, and ideology, we assess the extent to which AI can engage in authentic dialogue. With our discussion, we highlight, on the one hand, that in Bakhtinian terms, the discursive horizon of AI is limited to the corpus with which it was trained, blocking the authentic responsive dialogic nature of this interaction. On the other hand, we emphasize that this limitation - determined by AI creators - reproduces cultural and ideological patterns, potentially silencing various social voices. Finally, we stress the need to consider that AIs respond to the context of neoliberal transformations in production and consumption relations, characterized by the overlap between media, market, and the commodification of information. In light of this, it is ultimately the human being who must ethically assume responsibility for decision-making in the use and creation of technologies.
By positioning generative AI as a more-than-human conversational participant, the study invites qualitative researchers to think with non-human actors in the production of knowledge, extending post-qualitative approaches to digitally mediated research environments.
Wedsha Appadoo-Ramsamy· Qualitative Research Journal· 0 citations
Large language models (LLMs) are increasingly mediating public conversations through chatbots, search assistants, and content generation tools. In the context of grey zone competition, which involves hostile activity below the threshold of open conflict, LLM systems can be repurposed to amplify cognitive warfare by influencing individual and collective cognition to shape attitudes and behaviors. This article presents a philosophical-theological framework for evaluating “synthetic dialogue”, understood as machine-generated conversational interaction designed to shape beliefs, trust, and social bonds beliefs, trust, and social bonds. Drawing on the NATO-affiliated definition of cognitive warfare, the concept of information disorder, and dialogical philosophy, the paper argues that LLM-enabled persuasion and AI-driven deception (including deepfakes and plausible yet false claims) can erode the conditions of genuine dialogue by undermining epistemic trust and instrumentalizing individuals as targets. The theological-ethical analysis engages with recent Vatican documents on AI, truth, and peace, translating them into normative criteria for evaluating synthetic dialogue in grey zone contexts: truthfulness, transparency, responsibility, proportionality of influence, and respect for human dignity. Methodologically, the study combines conceptual analysis with an empirical probe that evaluates LLM outputs on curated philosophical and theological questions, as well as public-interest scenarios anchored in primary sources. The result is a draft evaluative model and a set of practical recommendations for researchers, platform designers, and religious communities seeking resilience against cognitive attacks.
It is predicted that for these topics to be explored accurately, it is necessary to decentralize the human and adopt a hybrid view of humans, society, and language.
Renan Monezi Lemes· Bakhtiniana: Revista de Estu...· 0 citations
Interactional naturalness of human-AI dialogue is a key factor in fostering meaningful communicative engagement. This study addresses this gap by adopting Conversation Analysis (CA) as a methodological framework to examine recorded speaking practice sessions between English as a Second or Foreign Language (ESL/EFL) learners and an AI tutoring system. Drawing on the principles of emergence, indexicality, and recipient design, the study investigates why and how interactional naturalness is violated in AI-mediated conversation. These findings reveal that while the AI tutor produces grammatically well-formed and topically relevant utterances, its conduct frequently diverges from the moment-by-moment, context-sensitive organization that characterizes natural human interaction. Particularly, the AI system fails to consistently align with emergent turn sequences, indexical references, and recipient-designed responses. The results indicate that current AI tutoring systems, despite advanced language models, have not yet achieved interactional naturalness as defined from a conversation-analytic perspective. Therefore, a shift in evaluation criteria from technical accuracy to interactional quality in the design and assessment of AI-based language tutors is called-for.
Wenting Li· Advances in Humanities Resea...· 0 citations
This workshop proposes a paradigm shift toward designing context-specific CUIs that actively support communicative success through pragmatic cues, and brings together perspectives from HCI and social sciences to explore how users appropriate conversational AI across domains.
Laura Spillner, Johanna Rockstroh, Paul Goerke et al.· International Conference on...· 0 citations
This volume, Communicating with AI: Philosophical Perspectives, examines a pressing question of our time: what does it mean to communicate with large language models (LLMs) such as ChatGPT, Claude, Gemini, and DeepSeek? With billions of human–AI interactions on the horizon, understanding whether these systems share our language, possess understanding, or function instead as novel instruments is an urgent theoretical and practical task. The essays collected here bring the tools of philosophy to bear on the distinctive challenges posed by AI. The contributions are organized into four parts. Part I probes the foundations of meaning, asking whether LLM outputs can be genuinely meaningful or whether they should be thought of as mere signals or measurements. Part II explores the possibility of alien semantics and externalist accounts of AI meaning, pushing the boundaries of how different from us an AI mind or language could be while still remaining intelligible. Part III investigates the social and normative dimensions of AI communication, focusing on trust, responsibility, and the risks of projecting human concepts onto machine outputs. Part IV turns to the future, considering the architectures of artificial minds and the unsettling ethical possibilities of superintelligent systems that may reason about values in ways inaccessible, or even hostile, to human interests. A central methodological theme runs throughout: the need to de-anthropomorphize our understanding of communication without emptying it of substance. By comparing AI with animals, aliens, and deities, the essays highlight both the promise and the limits of extending our concepts of mind and meaning beyond the human case. Collectively, the volume offers a rigorous philosophical framework for grappling with the metaphysical, epistemic, and ethical stakes of our emerging conversations with AI.
Unknown authors· 0 citations
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