In examining bias in artificial intelligence, we recognize its profound implications across various sectors, from finance to healthcare. Machine learning models often reflect societal prejudices, leading to disparities in outcomes such as hiring practices and automated decisions. Historical inequities in training data can result in flawed predictions that disproportionately affect marginalized groups. Additionally, our cognitive biases complicate interactions with AI, as we may over-rely on technology that perpetuates stereotypes. Understanding the interplay between machine and human biases is crucial for fostering ethical AI applications and addressing the significant ethical concerns that arise from biased decision-making processes.