Algorithmic bias refers to a systematic and repeatable error in AI systems that produces unfair outcomes, often favoring one group over another. This bias can emerge from skewed data used in training or from flawed assumptions in the model itself. For example, a recruitment algorithm may unfairly prioritize certain candidates due to historical data reflecting biased hiring practices. Addressing algorithmic bias is crucial to ensuring that AI systems are fair, transparent, and ethical. Businesses and developers must actively work to detect and mitigate bias, ensuring AI serves all users equitably.