The Role of Generative Artificial Intelligence in Forensic Linguistic

Abstract

The fast development of generative artificial intelligence (AI), specifically large language models (LLMs), has dramatically altered the future of the field of forensic linguistics. Forensic linguistics has traditionally been built upon the premise t hat people have some unique linguistic patterns, or idiolects, which can be used to determine authorship, detect deception, and interpret evidence In current times, the AI systems generate very advanced and human-like text casts doubt on the soundness of these core principles. This paper will critically look at the implication of generative AI in forensic linguisti cs and how it is transformative and what challenges it poses. On the one hand, AI technologies contribute to improving forensic examination based on automated stylometry, processing of vast corpus, and predictive linguistic modeling). Conversely, they make attributing authorship challenging by facilitating style imitating, scale-generating text, and obfuscating authorship. The paper adopts a qualitative research methodology which is based on recent literature review in fields such as methods of detecting AI-generated text, computer linguistics and law admissibility. Specific emphasis is placed on the drawbacks of the existing detection methods that are frequently characterized by a high rate of false-positives and are not very stable to adversarial interference. Algorithms bias, transparency, and Daubert standard concerns: The results indicate that forensic linguistics needs to change and address explainable AI models, hybrid human-computer analysis, and interdisciplinary partnerships. Although generative AI threatens traditional forensic practices to a large exte nt, it also gives ways to innovations and development. The paper concludes that, to ensure the integrity and viability of forensic linguistics in contemporary legal settings, it is crucial to adapt to these technological developments.