This study introduces SCOPE-Bias (Social Contextual Optimization for Evaluating Bias in AI Systems), an innovative framework aimed at addressing the shortcomings of traditional bias detection methods in contemporary large language models. Current techniques often struggle to recognize the dynamic and contextual aspects of biases present in conversational AI. In contrast, our framework offers a thorough solution for scalable and real-time bias assessment. The research utilizes transformer-based models such as MiniLM-L12-v2, DistilBERT, and BERT-base, which have been trained and evaluated on the CrowS- Pairs benchmark dataset to establish a strong baseline for bias detection performance. The SCOPE-Bias framework encompasses three pioneering elements: Firstly, it features a varied collection of social context scenarios that concentrate on sensitive attributes like race, gender, and socioeconomic status, facilitating comprehensive testing across diverse demographic dimensions. Second, we have developed an advanced dialog probing system that tracks the evolution of biases through multiple conversational exchanges, adeptly identifying subtle biases that surface during prolonged interactions. Third, there is a sophisticated semantic scoring engine that merges the CrowS-Pairs dataset with synthetic conversational data to scrutinize intricate linguistic bias patterns. Our experimental findings indicate that the MiniLM-L12-v2 model excels in bias detection, achieving superior performance (AUC: 0.76) while ensuring computational efficiency, surpassing both larger transformer models and traditional machine learning methods. The framework combines social contextual analysis with semantic evaluation, offering a comprehensive view of model behavior and enabling the detection of both explicit and implicit biases. This marks a significant improvement over static evaluation methods, providing 50ms latency for real-time applications. The research provides significant insights into the bias detection abilities of various model architectures, demonstrating notable effectiveness in recognizing gender-related biases (F1: 0.78) in contrast to less overt forms such as religious biases (F1: 0.70). The persistent challenge of false positives (18–22% across models) highlights areas for future improvement in bias mitigation techniques. This work contributes to the field of ethical AI by providing enterprises, policymakers, and developers with an interpretable, scalable framework for responsible AI deployment. The modular design of SCOPE-Bias supports extension to multilingual contexts and adaptation to emerging model architecture, making it a valuable tool for ongoing AI safety research. Future directions include integration of adversarial training methods and development of dynamic threshold optimization techniques to further enhance detection accuracy.