text: DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try to solve these 
  problems.[241] 
  
  Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution 
  of each feature to the output.[242] LIME can locally approximate a model's outputs with a simpler, 
  interpretable model.[243] Multitask learning provides a large number of outputs in addition to the target 
  classification. These other outputs can help developers deduce what the network has learned.[244] 
  Deconvolution, DeepDream and other generative methods can allow developers to see what different 
  layers of a deep network for computer vision have learned, and produce output that can suggest what the 
  network is learning.[245] For generative pre-trained transformers, Anthropic developed a technique based 
  on dictionary learning that associates patterns of neuron activations with human-understandable 
  concepts.[246] 