text: harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may 
  cause discrimination.[223] The field of fairness studies how to prevent harms from algorithmic biases. 
  
  On June 28, 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a 
  friend as "gorillas" because they were black. The system was trained on a dataset that contained very few 
  images of black people,[224] a problem called "sample size disparity".[225] Google "fixed" this problem by 
  preventing the system from labelling*anything*as a "gorilla". Eight years later, in 2023, Google Photos 
  still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and 
  Amazon.[226] 
  
  COMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant 
  becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial 
  bias, despite the fact that the program was not told the races of the defendants. Although the error rate for 
  both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the 
  system consistently overestimated the chance that a black person would re-offend and would 
  underestimate the chance that a white person would not re-offend.[227] In 2017, several researchers[l] 
  showed that it was mathematically impossible for COMPAS to accommodate all possible measures of 
  fairness when the base rates of re-offense were different for whites and blacks in the data.[229] 