text: A program can make biased decisions even if the data does not explicitly mention a problematic feature 
  (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping 
  history" or "first name"), and the program will make the same decisions based on these features as it 
  would on "race" or "gender".[230] Moritz Hardt said "the most robust fact in this research area is that 
  fairness through blindness doesn't work."[231] 
  
  Criticism of COMPAS highlighted that machine learning models are designed to make "predictions" that 
  are only valid if we assume that the future will resemble the past. If they are trained on data that includes 
  the results of racist decisions in the past, machine learning models must predict that racist decisions will 
  be made in the future. If an application then uses these predictions as*recommendations*, some of these 
  "recommendations" will likely be racist.[232] Thus, machine learning is not well suited to help make 
  decisions in areas where there is hope that the future will be*better*than the past. It is descriptive rather 
  than prescriptive.[m] 
  
  Bias and unfairness may go undetected because the developers are overwhelmingly white and male: 
  among AI engineers, about 4% are black and 20% are women.[225] 