combating racial bias
Combating racial bias in AI
There are many ways in which data can reflect biases. Data collection suffers from different biases that can result in the underrepresentation or overrepresentation of certain groups, populations or categories. This is especially the case when multiple data sets are combined and used in aggregate. Data might become tainted through the under-selection or over-selection of certain communities, groups or races. Give extra attention to historical data, especially in areas that have been riddled with prejudicial bias, to make sure new models created with this data don't incorporate those historical biases or injustices.