All Techniques

Explore our comprehensive collection of 121 techniques for responsible AI development.

121 techniques

		Models	Data Types	Description
SHapley Additive exPlanations	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an...
Permutation Importance	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	Permutation Importance quantifies a feature's contribution to a model's performance by randomly shuffling its values and...
Mean Decrease Impurity	Algorithmic	Architecture/tree Based Paradigm/supervised +1	Tabular	Mean Decrease Impurity (MDI) quantifies a feature's importance in tree-based models (e.g., Random Forests, Gradient...
Coefficient Magnitudes (in Linear Models)	Metric	Architecture/linear Models Paradigm/parametric +2	Tabular	Coefficient Magnitudes assess feature influence in linear models by examining the absolute values of their coefficients....
Integrated Gradients	Algorithmic	Architecture/neural Networks Paradigm/parametric +3	Any	Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of...
DeepLIFT	Algorithmic	Architecture/neural Networks Requirements/white Box +1	Any	DeepLIFT (Deep Learning Important FeaTures) explains neural network predictions by decomposing the difference between...
Layer-wise Relevance Propagation	Algorithmic	Architecture/neural Networks Paradigm/parametric +2	Any	Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to...
Contextual Decomposition	Algorithmic	Architecture/neural Networks/recurrent Requirements/white Box +1	Text	Contextual Decomposition explains LSTM and RNN predictions by decomposing the final hidden state into contributions from...
Taylor Decomposition	Algorithmic	Architecture/neural Networks Requirements/gradient Access +2	Any	Taylor Decomposition is a mathematical technique that explains neural network predictions by computing first-order and...
Sobol Indices	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	Sobol Indices quantify how much each input feature contributes to the total variance in a model's predictions through...
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	Any	This technique approximates a complex model by training a ridge regression (a linear model with L2 regularisation) on...
Partial Dependence Plots	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	Partial Dependence Plots show how changing one or two features affects a model's predictions on average. The technique...
Individual Conditional Expectation Plots	Visualization	Architecture/model Agnostic Requirements/black Box	Any	Individual Conditional Expectation (ICE) plots display the predicted output for individual instances as a function of a...
Saliency Maps	Algorithmic	Architecture/neural Networks Requirements/differentiable +1	Image	Saliency maps are visual explanations for image classification models that highlight which pixels in an image most...
Gradient-weighted Class Activation Mapping	Algorithmic	Architecture/neural Networks/convolutional Requirements/architecture Specific +2	Image	Grad-CAM creates visual heatmaps showing which regions of an image a convolutional neural network focuses on when making...
Occlusion Sensitivity	Algorithmic	Architecture/model Agnostic Requirements/black Box	Image	Occlusion sensitivity tests which parts of the input are important by occluding (masking or removing) them and seeing...
Classical Attention Analysis in Neural Networks	Algorithmic	Architecture/neural Networks/recurrent Requirements/architecture Specific +1	Any	Classical attention mechanisms in RNNs and CNNs create alignment matrices and temporal attention patterns that show how...
Factor Analysis	Algorithmic	Architecture/model Agnostic Paradigm/unsupervised +1	Tabular	Factor analysis is a statistical technique that identifies latent variables (hidden factors) underlying observed...
Principal Component Analysis	Algorithmic	Architecture/model Agnostic Paradigm/unsupervised +1	Any	Principal Component Analysis transforms high-dimensional data into a lower-dimensional representation by finding the...

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