Systematic biases and distorted representations that artificial intelligence models can learn and reproduce from training data, perpetuating prejudices related to gender, ethnicity, age, or other demographic characteristics. Includes issues of fairness, algorithmic discrimination, non-representative datasets, and mitigation techniques such as debiasing, fairness constraints, and AI model auditing.