Microsoft Azure AI Fundamentals AI-900 Practice Question

Use training data that includes a wide range of demographic groups - This is the correct answer. Ensuring that the training data is diverse and includes a wide range of demographic groups helps reduce biases and ensures that the automated loan approval system treats all applicants fairly. This approach ensures that the model learns from a broad spectrum of data, which can help minimize unfairness in the system.

Exclude any features related to personal characteristics from the data - While this may seem like a good way to prevent bias, excluding personal characteristics (such as age, gender, or race) entirely could lead to a model that lacks important context for making fair and informed decisions.

Increase the complexity of the algorithm to improve accuracy - Increasing algorithm complexity might improve accuracy, but it does not directly address fairness.

Expand the dataset by collecting more data of the same type - Expanding the dataset with more of the same type of data might not necessarily improve fairness if the data itself is biased or unrepresentative.