Data Diversity

More Diverse Data Makes for Smarter AI

- Overview

Data diversity refers to the variety of types of elements in data, including different types of data storage. It can also refer to the different forms that data can take, such as structured, semi-structured, and unstructured data.

Data diversity is important for machine learning (ML) models and artificial intelligence (AI) models because more diverse training data can lead to better model performance and help mitigate bias. 

For example, a facial recognition algorithm that only learns facial features of one population may struggle to recognize other populations.

Here are some ways to increase data diversity:

• Break down silos
• Establish enterprise-wide data repositories and governance policies to streamline access.
• Transform unstructured data
• Use tools to convert documents, emails, images, videos, and voice recordings into usable formats.
• Diversify training data
• Use the predictions of multiple forward and backward models and merge them with the original dataset.

- Data Diversity in AI

Data diversity in AI is important for avoiding bias and overfitting, which can lead to AI models making mistakes. Diverse data can come from many sources, including partners, customers, data providers, or automation. It can also include data from different perspectives, such as cultural context or different time frames. 

Here are some ways that diverse data can help AI models:

• Avoid bias: Diverse data can help AI models avoid internal bias and ensure that
• they are more inclusive. For example, if AI models are trained on data that doesn't include people of color, they may perpetuate existing biases.
• Prevent overfitting: Overfitting occurs when an AI model only learns from the data it was trained on, and can't provide results when tested on new data. Diverse data can help prevent this by providing samples with enough information to train the model.
• Increase accuracy: Diverse data can also make AI models more accurate by incorporating diverse human intelligence and experience. This is supported by the diversity prediction theorem, which states that when a group is diverse, the error of the crowd is small.

[More to come ...]