Why is diversity important in Al training data?

Why is diversity important in Al training data?

A. To make Al models cheaper to develop

B. To reduce the storage requirements for data

C. To ensure the model can generalize across different scenarios

D. To increase the model’s speed of computation

View Answer

Answer: C

Explanation:

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.

Reference: 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.