Artificial Intelligence (AI) has become a cornerstone of modern technology, influencing decisions in fields ranging from healthcare to hiring. However, as AI systems gain prominence, concerns about bias in algorithms have grown. AI bias occurs when an algorithm produces prejudiced outcomes due to the data it was trained on or the way it was designed. This article explores the causes of AI bias, real-world examples, and strategies for creating fair and unbiased AI systems. What is AI Bias? AI bias refers to systematic and unfair discrimination embedded in AI systems. Unlike human bias, which arises from subjective judgment, AI bias is often the unintended result of skewed data, flawed algorithms, or oversight during development. The consequences can range from mild inconveniences to serious injustices, such as discrimination in employment or biased legal judgments. Causes of AI Bias Biased Training Data AI models rely on historical data to learn patterns and…

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