All Techniques

Explore our comprehensive collection of 162 techniques for responsible AI development.

162 techniques

		Models	Data Types	Description
SHapley Additive exPlanations	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	SHAP (SHapley Additive exPlanations) explains one prediction by sharing out the gap between that prediction and a...
Permutation Importance	Algorithmic	Architecture/model Agnostic Requirements/black Box	Tabular	Permutation Importance measures how much a trained model relies on each input feature. For each feature, the values in a...
Mean Decrease Impurity	Algorithmic	Architecture/tree Based Paradigm/supervised +1	Tabular	Mean Decrease Impurity (MDI) scores how much each feature contributes to a tree-based model, such as a random forest, by...
Coefficient Magnitudes (in Linear Models)	Metric	Architecture/linear Models Paradigm/parametric +2	Tabular	Coefficient Magnitudes explains a linear model by reading its fitted coefficients. Each coefficient shows how much the...
Integrated Gradients	Algorithmic	Architecture/neural Networks Architecture/neural Networks/transformer +4	Image Tabular Text	Integrated Gradients assigns input features scores for a prediction relative to a chosen reference input, called the...
DeepLIFT	Algorithmic	Requirements/model Internals Architecture/neural Networks +2	Any	DeepLIFT (Deep Learning Important FeaTures) explains a single prediction of a neural network by dividing the difference...
Layer-wise Relevance Propagation	Algorithmic	Requirements/model Internals Architecture/neural Networks +3	Any	Layer-wise Relevance Propagation (LRP) explains a neural network's prediction by working backwards from the output to...
Contextual Decomposition	Algorithmic	Architecture/neural Networks/recurrent Requirements/white Box +1	Text	Contextual Decomposition explains why an LSTM, a neural network that reads text one word at a time, made a particular...
Taylor Decomposition	Algorithmic	Requirements/model Internals Architecture/neural Networks +3	Any	Taylor Decomposition explains one prediction of a neural network by splitting its output score into a contribution from...
Sobol Indices	Algorithmic	Architecture/model Agnostic Requirements/black Box	Tabular	Sobol indices show how much of the variation in a model's output can be traced to each of its inputs. The analyst sets a...
Local Interpretable Model-Agnostic Explanations	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by approximating the complex...
Ridge Regression Surrogates	Algorithmic	Architecture/model Agnostic Requirements/black Box	Tabular	A ridge regression surrogate approximates a complex model with a linear equation fitted to the complex model's own...
Partial Dependence Plots	Visualisation	Architecture/model Agnostic Requirements/black Box	Tabular	Partial Dependence Plots show how changing one or two features affects a model's predictions on average. The technique...
Individual Conditional Expectation Plots	Visualisation	Architecture/model Agnostic Requirements/black Box	Tabular	Individual Conditional Expectation (ICE) plots display the predicted output for individual instances as a function of a...
Gradient Saliency	Algorithmic	Architecture/neural Networks Requirements/differentiable +2	Image	Gradient saliency (also called vanilla gradient saliency) is the simplest gradient-based explanation for image...
Gradient-weighted Class Activation Mapping	Algorithmic	Architecture/neural Networks/convolutional Requirements/architecture Specific +3	Image	Grad-CAM (Gradient-weighted Class Activation Mapping) draws a heatmap over an image showing which regions most raise a...
Occlusion Sensitivity	Algorithmic	Architecture/model Agnostic Requirements/black Box	Image	Occlusion sensitivity shows which parts of an input a model's prediction is sensitive to. A region of the input, such as...
Attention Weight Analysis	Algorithmic	Architecture/neural Networks/recurrent Architecture/neural Networks/transformer +2	Image Text	Attention weight analysis reads the weights an attention-based neural network assigns to the parts of its input, to see...
Factor Analysis	Algorithmic	Architecture/model Agnostic Paradigm/unsupervised +1	Tabular	Factor analysis looks for a small number of hidden factors that explain why a larger set of measured variables, such as...
Principal Component Analysis	Algorithmic	Architecture/model Agnostic Paradigm/unsupervised +1	Any	Principal component analysis (PCA) summarises a dataset that has many numeric features by finding a few new axes along...

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