Example Concept: AI models in finance can inadvertently perpetuate biases present in training data, leading to unfair or inaccurate decision-making. Additionally, the "black box" nature of some AI algorithms can make it difficult to understand how decisions are made, posing challenges for transparency and accountability. Ensuring data quality and adhering to regulatory standards are critical to mitigating these risks.