Artificial intelligence (AI) holds immense promise in transforming various aspects of our society, spanning healthcare, education, and transportation. However, the widespread deployment of AI systems also presents significant ethical and societal challenges, particularly in relation to bias. Bias within AI systems has the potential to perpetuate and exacerbate existing inequalities, leading to adverse outcomes for individuals and marginalized groups. This paper explores the critical issue of bias detection and mitigation within Automatic Speech Recognition (ASR) systems. We highlight the importance of identifying and addressing bias in ASR to ensure fairness and equity. Our proposed approach involves the development of a fair AI framework that utilizes statistical methods to detect and quantify biases within both the training dataset and the ASR model itself. Additionally, we introduce custom methods for mitigating bias in speech data, aiming to counteract any disparities that may arise from underrepresentation within the training dataset. Empirical evidence demonstrates a clear correlation between bias in the training dataset and bias in the resulting ASR model. By addressing bias at the dataset level, we can significantly reduce the likelihood of biased outcomes in ASR applications. Our ultimate objective is to provide a comprehensive understanding of bias in ASR and to offer practical guidance for researchers, developers, and practitioners committed to creating fair and unbiased ASR systems.