How to Reduce Bias in AI with a Focus on Training Data

Algorithmic bias in AI is a pervasive problem. You can likely recall biased algorithm examples in the news, such as speech recognition not being able to identify the pronoun "hers" but being able to identify "his" or face recognition software being less likely to recognize people of color. While entirely eliminating bias in AI is not possible, it's essential to know not only how to reduce bias in AI, but actively work to prevent it. Knowing how to mitigate bias in AI systems stems from understanding the training data sets that are used to generate and evolve models. In our 2020 State of AI and Machine Learning Report, only 15% of companies reported data diversity, bias reduction, and global scale for their AI as "not important."