Hallucination risk and trustworthiness of generative AI systems based on IVMPF-MABAC decision-making strategies

Hallucination risk and trustworthiness of a generative artificial intelligence system play an important and vital role in daily-life scenarios. The terms Hallucination risk and trustworthiness are very famous for the generative artificial intelligence techniques to confirm that the final results or outputs are safe, accurate, and reliable for users or customers, particularly in education, finance, and healthcare. The main purpose of this manuscript is to evaluate the best and worst decisions among the decisions of Gemini (Google DeepMind), Copilot (GitHub/Microsoft), LLaMA (Meta), Claude (Anthropic), and ChatGPT (OpenAI) based on the following criteria, such as data privacy, explainability, Hallucination risk, reliability, and bias control. Therefore, the system of interval-valued m-polar fuzzy-multi-attribute border approximation area comparison technique for algebraic information is developed. The invented model is very famous and reliable for coping with awkward and complex information. Therefore, for the valuation or the construction of the derived technique, this study aims to develop the novel technique of interval-valued m-polar fuzzy information and also investigate their operations based on algebraic norms for the construction of the averaging operator and geometric operator. Lastly, this study also works on the analysis of the numerical examples and the implementation of the comparative analysis among proposed and prevailing frameworks to derive the validity and effectiveness of the introduced approaches.