Algorithmic bias is one of the most critical challenges in AI systems, as models may produce unfair or inaccurate decisions due to biased data or design choices. Bias occurs when training data reflects historical or social imbalances, leading algorithms to learn and replicate those patterns automatically. This has been observed in hiring systems, credit scoring, and facial recognition, where performance gaps across demographic groups have been documented. The danger of algorithmic bias is that it can remain hidden while still influencing high-stakes decisions. Automated outputs may appear objective even when they are shaped by skewed data. Therefore, algorithmic bias is not only a technical issue but also a social and ethical concern. Mitigation requires action across the lifecycle: careful data collection, preprocessing, feature selection, and cross-group evaluation. Statistical debiasing techniques and fairness constraints are increasingly used. International guidelines recommend regular fairness audits before and after deployment. Recognizing and addressing algorithmic bias is essential for more equitable digital decision-making.