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In an era increasingly shaped by artificial intelligence (AI), the necessity for unbiased decision-making from AI systems intensifies. Recognizing the inherent biases in humandecision-making is evident through various psychological theories. Prospect Theory, prominently featured among them, utilizes a probability weighing function (PWF) to gain insights into human decision processes. This observation prompts an intriguing question: Can this framework be extended to comprehend AI decision-making? This study employs a systematic Gibbs sampler method to measure probability weighing function of AI and validate this methodology against a dataset comprising 1 million distinct AI decision strategies. Subsequently, exemplification of its application on Recurrent Neural Networks (RNN) and Artificial Neural Networks (ANN) is seen. This allows us to discern the nuanced shapes of the Probability Weighting Functions (PWFs) inherent in ANN and RNN, thereby facilitating informed speculation on the potential presence of “probability bias” within AI. In conclusion, this research serves as a foundational step in the exploration of "probability bias" in AI decision-making. The demonstrated reliability of the systematic Gibbs sampler method significantly contributes to ongoing research, primarily by enabling the extraction of Probability Weighting Functions (PWFs). The emphasis here lies in laying the groundwork –obtaining the PWFs from AI decision processes. The subsequent phases, involving in-depth understanding and deductive conclusions about the implications of these PWFs, fall outside the current scope of this study. With the ability to discern the shapes of PWFs for AI, this research paves the way for future investigations and various tests to unravel the deeper meaning of probability bias in AI decision-making.