Why is diversity important in Al training data?

Diversity in AI training data is crucial for developing robust and fair AI models. The correct answer is option C. Here's why:

Generalization: A diverse training dataset ensures that the AI model can generalize well across different scenarios and perform accurately in real-world applications.

Bias Reduction: Diverse data helps in mitigating biases that can arise from over-representation or under-representation of certain groups or scenarios.

Fairness and Inclusivity: Ensuring diversity in data helps in creating AI systems that are fair and inclusive, which is essential for ethical AI development.

References:

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org.

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys (CSUR), 54(6), 1-35.