What Is Data Bias and How to Avoid It

Data bias can have notable implications for research and practical applications. For example, in a Facebook scandal, its AI shockingly asked users if they wanted to continue seeing videos about primates after watching a video featuring Black men.

Data bias refers to data sets that don't represent the population in study. Models trained on biased data could contain prejudice. That's why AI Researchers and Data Scientists must be vigilant to ensure models don't contain any bias.

The most common avenues for collecting training data:

• Paying for data sets
• Using public data sets
• Sourcing open source content
• Using in-person or field-collected data sets

A combination of all four will provide the best training data.