Data bias is a common problem in AI and Machine Learning applications, often occurring unintentionally. This article will look at three ways to limit data bias: collecting data from a variety of sources, ensuring data is diverse, and monitoring real-world performance. Data bias can have significant implications for research and practical applications.
Collect data from a variety of sources
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.
- If your model involves predictions relating to speech, make sure that the data set is robust to all environments and background noise.
Make sure data is diverse
A variety of sources and diverse data within each source is beneficial, especially if you rely on open-source data
- Sourcing diverse data may prove difficult, so it’s important that these first two recommendations go hand in hand
- Have diverse data in each source
Monitor real-world performance
Look for any areas where bias may have crept in
- Take time to retrain with new datasets to weed out any problem areas
- Collecting data from many sources, ensuring a diverse data set, and monitoring model performance will increase the likelihood that your models will perform in the real world
