XAI
graph LR; Node_0[deep inside convolutional networks visualising image classification models and saliency maps] Node_31[striving for simplicity the all convolutional net] Node_35[the unreliability of saliency methods] Node_3[real time image saliency for black box classifiers] Node_33[network dissection quantifying interpretability of deep visual representations] Node_5[understanding deep networks via extremal perturbations and smooth masks] Node_36[methods for interpreting and understanding deep neural networks] Node_38[explaining explanations an overview of interpretability of machine learning] Node_8[explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai] Node_9[did the model understand the question] Node_29[smoothgrad removing noise by adding noise] Node_34[how important is a neuron] Node_12[computationally efficient measures of internal neuron importance] Node_30[learning important features through propagating activation differences] Node_46[a unified approach to interpreting model predictions] Node_15[influencedirected explanations for deep convolutional networks] Node_40[towards better understanding of gradientbased attribution methods for deep neural networks] Node_17[a survey on neural network interpretability] Node_18[opportunities and challenges in explainable artificial intelligence xai a survey] Node_19[explainable artificial intelligence for tabular data a survey] Node_47[peeking inside the blackbox a survey on explainable artificial intelligence xai] Node_21[explainable artificial intelligence xai in deep learningbased medical image analysis] Node_22[a systematic review of human computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques] Node_42[explainer a visual analytics framework for interactive and explainable machine learning] Node_24[visual analytics for humancentered machine learning] Node_25[exploiting explanations for model inversion attacks] Node_26[if only we had better counterfactual explanations five key deficits to rectify in the evaluation of counterfactual xai techniques] Node_27[visualizing and understanding convolutional networks] Node_28[network in network] Node_32[sanity checks for saliency maps] Node_37[interpretation of neural networks is fragile] Node_39[explanations can be manipulated and geometry is to blame] Node_41[distilling the knowledge in a neural network] Node_43[why should i trust you explaining the predictions of any classifier] Node_44[explainable ai beware of inmates running the asylum or how i learnt to stop worrying and love the social and behavioural sciences] Node_45[explanation in humanai systems a literature metareview synopsis of key ideas and publications and bibliography for explainable ai] Node_0 --> Node_27 Node_31 --> Node_28 Node_31 --> Node_27 Node_35 --> Node_29 Node_35 --> Node_30 Node_35 --> Node_27 Node_3 --> Node_31 Node_3 --> Node_27 Node_33 --> Node_27 Node_5 --> Node_31 Node_5 --> Node_29 Node_5 --> Node_32 Node_5 --> Node_27 Node_36 --> Node_31 Node_36 --> Node_27 Node_38 --> Node_33 Node_38 --> Node_30 Node_38 --> Node_27 Node_8 --> Node_28 Node_9 --> Node_31 Node_9 --> Node_30 Node_29 --> Node_30 Node_34 --> Node_30 Node_12 --> Node_34 Node_12 --> Node_30 Node_30 --> Node_31 Node_46 --> Node_30 Node_15 --> Node_31 Node_15 --> Node_35 Node_15 --> Node_34 Node_40 --> Node_36 Node_40 --> Node_29 Node_40 --> Node_30 Node_40 --> Node_27 Node_17 --> Node_31 Node_17 --> Node_37 Node_17 --> Node_35 Node_17 --> Node_33 Node_17 --> Node_36 Node_17 --> Node_38 Node_17 --> Node_39 Node_17 --> Node_32 Node_17 --> Node_30 Node_17 --> Node_27 Node_17 --> Node_40 Node_18 --> Node_31 Node_18 --> Node_37 Node_18 --> Node_35 Node_18 --> Node_32 Node_18 --> Node_30 Node_18 --> Node_27 Node_18 --> Node_40 Node_19 --> Node_30 Node_47 --> Node_41 Node_47 --> Node_33 Node_47 --> Node_29 Node_47 --> Node_27 Node_21 --> Node_31 Node_21 --> Node_32 Node_21 --> Node_27 Node_22 --> Node_42 Node_42 --> Node_30 Node_42 --> Node_40 Node_24 --> Node_42 Node_25 --> Node_33 Node_25 --> Node_38 Node_25 --> Node_29 Node_25 --> Node_32 Node_25 --> Node_30 Node_25 --> Node_27 Node_26 --> Node_43 Node_26 --> Node_44 Node_26 --> Node_45 Node_26 --> Node_46 Node_26 --> Node_27 Node_26 --> Node_47