The question asks about a fundamental task for ethical AI deployment, and provides a Python code snippet that checks for class imbalance, which is a form of bias. The code snippet: `python def check_bias(data, labels): Sample check for class imbalance unique, counts = np.unique(labels, return_counts=True) print(dict(zip(unique, counts))) ` This function is designed to identify if there's an uneven distribution of classes in the labels (target variable) of a dataset. An imbalance in the training data can lead to an AI model that performs poorly or unfairly for the minority classes, thus introducing bias into the model's predictions. The question asks to choose one of the following options. Based on the visible option and the context: Identifying and mitigating any potential biases in data. This option directly addresses the purpose of the provided code snippet and is a critical aspect of ethical AI development and deployment. Recognizing and correcting biases in the data is essential to ensure fairness, accuracy, and non-discrimination in AI systems. Therefore, the correct answer is: Identifying and mitigating any potential biases in data.