Investigating Stance Marking in Computer-Assisted AI Chatbot Discourse
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2025, Vol 4, Issue 1
Abstract
Stance, a critical discourse marker, reflects the expression of attitudes, feelings, evaluations, or judgments by speakers or writers toward a topic or other participants in a conversation. This study investigates the manifestation of stance in the discourse of four prominent artificial intelligence (AI) chatbots—ChatGPT, Gemini, MetaAI, and Bing Copilot—focusing on three dimensions: interpersonal stance (how chatbots perceive one another), epistemic stance (their relationship to the topic of discussion), and style stance (their communicative style). Through a systematic analysis, it is revealed that these chatbots employ various stance markers, including hedging, self-mention, power dominance, alignment, and face-saving strategies. Notably, the use of face-saving framing by AI models, despite their lack of a genuine “face,” highlights the distinction between authentic interactional intent and the reproduction of linguistic conventions. This suggests that stance in AI discourse is not a product of subjective intent but rather an inherent feature of natural language. However, this study extends the discourse by examining stance as a feature of chatbot-to-chatbot communication rather than human-AI interactions, thereby bridging the gap between human linguistic behaviors and AI tendencies. It is concluded that stance is not an extraneous feature of discourse but an integral and unavoidable aspect of language use, which chatbots inevitably replicate. In other words, if chatbots must use language, then pragmatic features like stance are inevitable. Ultimately, this raises a broader question: Is it even possible for a chatbot to produce language devoid of stance? The implications of this research underscore the intrinsic connection between language use and pragmatic features, suggesting that stance is an inescapable component of any linguistic output, including that of AI systems.
Authors and Affiliations
Kayode Victor Amusan
Keywords
Related Articles
- EP ID EP767822
- DOI https://doi.org/10.56578/ataiml040104
- Views 16
- Downloads 0
Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine
This paper aimed to realize intelligent diagnosis of obstetric diseases using electronic medical records (EMRs). The Optimized Kernel Extreme Machine Learning (OKEML) technique was proposed to rebalance data. The hybrid...
Investigating Stance Marking in Computer-Assisted AI Chatbot Discourse
Stance, a critical discourse marker, reflects the expression of attitudes, feelings, evaluations, or judgments by speakers or writers toward a topic or other participants in a conversation. This study investigates the ma...
A Dual-Selective Channel Attention Network for Osteoporosis Prediction in Computed Tomography Images of Lumbar Spine
Osteoporosis is a common systemic bone disease with insidious onset and low treatment efficiency. Once it occurs, it will increase bone fragility and lead to fractures. Computed tomography (CT) is a non-invasive medical...
Augmenting Diabetic Retinopathy Severity Prediction with a Dual-Level Deep Learning Approach Utilizing Customized MobileNet Feature Embeddings
Diabetic retinopathy, a severe ocular disease correlated with elevated blood glucose levels in diabetic patients, carries a significant risk of visual impairment. The essentiality of its timely and precise severity class...
Enhancing Non-Invasive Diagnosis of Endometriosis Through Explainable Artificial Intelligence: A Grad-CAM Approach
Significant advancements in artificial intelligence (AI) have transformed clinical decision-making, particularly in disease detection and management. Endometriosis, a chronic and often debilitating gynecological disorder...