applicable models

black box

Only requires input-output access, no internal model access needed

76 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...
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...
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...
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...
t-SNE	Visualization	Architecture/model Agnostic Requirements/black Box	Any	t-SNE (t-Distributed Stochastic Neighbour Embedding) is a non-linear dimensionality reduction technique that creates 2D...
UMAP	Visualization	Architecture/model Agnostic Requirements/black Box	Any	UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction technique that creates 2D...
Prototype and Criticism Models	Algorithmic	Architecture/model Agnostic Paradigm/supervised +3	Any	Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes...
ANCHOR	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	ANCHOR generates high-precision if-then rules that explain individual predictions by identifying the minimal set of...
Permutation Tests	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	Permutation tests assess the statistical significance of observed results (such as model accuracy, feature importance,...
Demographic Parity Assessment	Algorithmic	Architecture/model Agnostic Paradigm/supervised +1	Any	Demographic Parity Assessment evaluates whether a model produces equal positive prediction rates across different...
Counterfactual Fairness Assessment	Algorithmic	Architecture/model Agnostic Paradigm/supervised +1	Any	Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's...
Sensitivity Analysis for Fairness	Algorithmic	Architecture/model Agnostic Paradigm/supervised +2	Any	Sensitivity Analysis for Fairness systematically evaluates how model predictions change when sensitive attributes or...
Differential Privacy	Algorithmic	Architecture/model Agnostic Requirements/black Box	Any	Differential privacy provides mathematically rigorous privacy protection by adding carefully calibrated random noise to...
Prediction Intervals	Algorithmic	Architecture/model Agnostic Paradigm/supervised +1	Any	Prediction intervals provide a range of plausible values around a model's prediction, expressing uncertainty as 'the...

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