Example Concept: AI models can inadvertently incorporate biases present in training data, leading to skewed decision-making. Additionally, poor data quality can result in inaccurate predictions, while over-reliance on AI without human validation may overlook nuanced insights that require human judgment. Regular audits and a balanced approach combining AI insights with human expertise are essential to mitigate these risks.