Jumio BrandVoice: 5 Ways To Keep AI Bias Out Of Online Identity Verification

When bias becomes embedded in machine learning models, it can have an adverse impact on our daily lives. It's exhibited in the form of exclusion, such as certain groups being denied loans or not being able to use the technology. As AI continues to become more a part of our lives, the risks from bias only grow larger. In the context of facial recognition, demographic traits such as race, age, gender, socioeconomic factors, and even the quality of the camera/device can impact software's ability to compare one face to a database of faces. In these types of surveillance, the quality and robustness of the underlying database is what can fuel bias in the AI models.