Artificial Intelligence (AI) has become a driving force behind decision-making in industries such as healthcare, finance, education, and recruitment. While these technologies bring efficiency and innovation, they also pose a major concern: bias. Bias in AI systems can lead to unfair outcomes, discrimination, and a lack of trust among users. As AI continues to integrate into daily life, avoiding bias in algorithms has become a critical priority for developers, businesses, and policymakers alike. This article explores the nature of bias in AI systems, strategies to minimize it, and the role of ethical responsibility in creating fair, transparent technologies. Understanding Bias in AI Bias in AI occurs when algorithms produce unfair or prejudiced outcomes. This typically happens because AI systems learn from historical data that may reflect societal inequalities. If an algorithm is trained on biased data, it can unintentionally replicate or even amplify those biases in real-world applicat...