Veritas AI: The ChatGPT Polygraph

Anshit Mukherjee *

Department of Computer Science, Abacus Institute of Engineering and Management, Mogra, Hooghly, India.

*Author to whom correspondence should be addressed.


Abstract

Aims: The objective of Veritas AI is to revolutionize the domain of lie detection through the deployment of a cutting-edge algorithm within the realms of computational linguistics and artificial intelligence.

Study Design: Veritas AI is conceptualized as a groundbreaking framework that integrates advanced syntactic and semantic analysis, leveraging generative pre-trained transformers to identify linguistic cues indicative of deception.

Place and Duration of Study: The research underpinning Veritas AI’s algorithm was meticulously executed at the Abacus CSE Lab over a period from December 2022 to March 2024, ensuring a robust empirical foundation for the system’s validation and optimization.

Methodology: Employing a deep learning neural network at its core, Veritas AI is trained on a diverse dataset comprising both truthful and deceptive dialogues. This training is complemented by multimodal biometric interrogation techniques and sophisticated natural language processing algorithms.

Results: The empirical results underscore Veritas AI’s unparalleled accuracy in discerning truth, marked by its ability to provide real-time adaptive feedback and maintain robust performance across various communication scenarios.

Conclusion: In conclusion, Veritas AI stands as a testament to the symbiotic potential of human ingenuity and machine learning. Its precision-engineered algorithm, underpinned by empirical validation, heralds a transformative leap in the field of automated veracity assessment, setting a new benchmark for truth analysis in the digital age.

Keywords: ChatGPT, context awareness, text analysis, lie-detection


How to Cite

Mukherjee , A. (2024). Veritas AI: The ChatGPT Polygraph. Asian Journal of Research in Computer Science, 17(6), 157–177. https://doi.org/10.9734/ajrcos/2024/v17i6465

Downloads

Download data is not yet available.

References

King A, ChatGPT A. ChatGPT: A new tool for academic writing and plagiarism detection. Journal of Educational Technology & Society. 2023;26(1):1-12.

Watters P, Lemanski J. Evaluating the legitimacy of ChatGPT responses: A research on text-matching skills. International Journal of Integrity in Education. 2023;19(1):15.

Verigin BL, Meijer EH, Bogaard G, Vrij A. Lie prevalence, lie characteristics, and strategies of self-reported good liars. PLOS ONE. 2019;14(12):e0225566.

In 2023, Gabashvili, I. ChatGPT: It is the duty of editors and publishers to detect plagiarism in scholarly publications. 51(6), 2103-2104, Annals of Biomedical Engineering.

Testoni L, Debole F, Poesio M. Fighting fire with fire: Can ChatGPT detect AI-generated text? Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022;123-132.

Cook M, Layton R, Morrey A, Petersen D, Holt-Lunstad J, Workman C, Andrews C, Barton B, Smith TB, Cook M. A meta-analysis of 106 randomized controlled trials examined the impact of psychological support treatments on survival in inpatient and outpatient healthcare settings. PLOS Medicine. 2021;18(5):1003595. DOI: 10.1371/journal.pmed.1003595

Jones DS, Silverman D. (Eds.). Qualitative research (5th ed.). SAGE Publications Ltd; 2022.

Available:https://uk.sagepub.com/en-gb/eur/qualitative-research/book271731

Chang J, Lee J. Meant to be. MyDramaList; 2023 Available:https://mydramalist.com/748417-fate-of-heaven

Patel A, Gupta R. Voice stress analysis techniques and applications. Journal of Voice Studies. 2020;34(2):110-122.

Kim J, Park S. Eye-tracking and gaze patterns in communication. Journal of Vision Research. 2019;58(4):204-213.

Rahman M, et al. Multimodal fusion for speech and facial cues analysis. International Journal of Human-Computer Interaction. 2022;38(7):625-635

Garcia OF, Rodriguez Y. Linguistic complexity metrics and their applications. Journal of Language and Social Psychology. 2018;37(5):589-601

Wu C, Chen L. Sentiment analysis in chat logs: Methods and findings. Journal of Computer-Mediated Communication. 2021; 26(1):22-37.

Zhang Y, ChatGPT A. ChatGPT: A novel approach for sentiment analysis and emotion detection. Expert Systems with Applications. 2023;180: 115270.

Lee J, ChatGPT A. ChatGPT: A powerful tool for text summarization and paraphrasing. Information Processing & Management. 2023;60(3):102667.

Chen X, ChatGPT A. ChatGPT: A game-changer for natural language generation and understanding. Artificial Intelligence. 2023;300:103599.

Singh R, ChatGPT A. ChatGPT: A breakthrough for question answering and dialogue systems. Knowledge-Based Systems. 2023;224:107092.

Wang Z, ChatGPT A. ChatGPT: A challenge for plagiarism detection and authorship verification. Journal of the Association for Information Science and Technology. 2023;74(4): 567-578.

Liu Y, ChatGPT A. ChatGPT: A revolution for machine translation and cross-lingual understanding. Machine Translation. 2023; 37(1):1-16.

Watters P, Lemanski J. ChatGPT across disciplines: A systematic review of applications, limitations, and ethical considerations. Computers in Human Behavior. 2023;125:106911.

Gabashvili IS. The effect and uses of ChatGPT: A thorough assessment of literature reviews. Human Behavior and Computers. 2023;125:106911.

Kim S, Chat GPT A. Chat GPT: A paradigm shift for text classification and sentiment analysis. Neural Networks. 2023;138:392-403.

Li J, Chat GPT A. ChatGPT: A miracle for text mining and information extraction. Data & Knowledge Engineering. 2023;136: 101980.

Park H, Chat GPT A. ChatGPT: A wonder for text simplification and readability assessment. Computer Speech & Language. 2023;69:101222.

Yang L, Chat GPT A. ChatGPT: A marvel for text generation and evaluation. Natural Language Engineering. 2023;29(2):237-254.