text: **Lack of transparency**
  Many AI systems are so complex that their designers cannot explain how they reach their decisions.[235] 
  Particularly with deep neural networks, in which there are a large amount of non-linear relationships 
  between inputs and outputs. But some popular explainability techniques exist.[236] 
  
  It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. 
  There have been many cases where a machine learning program passed rigorous tests, but nevertheless 
  learned something different than what the programmers intended. For example, a system that could 
  identify skin diseases better than medical professionals was found to actually have a strong tendency to 
  classify images with a ruler as "cancerous", because pictures of malignancies typically include a ruler to 
  show the scale.[237] Another machine learning system designed to help effectively allocate medical 
  resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. 
  Having asthma is actually a severe risk factor, but since the patients having asthma would usually get 
  much more medical care, they were relatively unlikely to die according to the training data. The 
  correlation between asthma and low risk of dying from pneumonia was real, but misleading.[238] 
  
  People who have been harmed by an algorithm's decision have a right to an explanation.[239] Doctors, for 
  example, are expected to clearly and completely explain to their colleagues the reasoning behind any 
  decision they make. Early drafts of the European Union's General Data Protection Regulation in 2016 
  included an explicit statement that this right exists.[n] Industry experts noted that this is an unsolved 
  problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has 
  no solution, the tools should not be used.[240] 