Example Concept: AI models learn patterns from training data, which may contain biases reflecting societal or historical prejudices. As a result, the AI's outputs can be skewed if the data is not carefully curated and balanced. Ensuring objectivity requires ongoing efforts to identify and mitigate these biases through techniques like diverse data sampling and fairness algorithms.