The AI Explainability 360 (AIX360) toolkit is a Python library that offers a wide range of explanation types as well as some explainability metrics. AIX360 offers excellent guidance material, an interactive demo as well as developer tutorials. What’s particularly good about this material is that it stimulates reflection on which type of explanation is appropriate, not only from a technical point of view, but also with respect to the target explainer and explainee. Read more...

Alibi is an open-source Python library that supports various interpretability techniques and a broad array of explanation types. The README already provides an overview of the supported methods and when they are applicable. The following table with supported methods is copied from the README (slightly abbreviated): Supported methods Method Models Explanations Classification Regression Tabular Text Images Categorical features ALE BB global ✔ ✔ ✔ Anchors BB local ✔ ✔ ✔ ✔ ✔ CEM BB* TF/Keras local ✔ ✔ ✔ Counterfactuals BB* TF/Keras local ✔ ✔ ✔ Prototype Counterfactuals BB* TF/Keras local ✔ ✔ ✔ ✔ Integrated Gradients TF/Keras local ✔ ✔ ✔ ✔ ✔ ✔ Kernel SHAP BB local global ✔ ✔ ✔ ✔ Tree SHAP WB local global ✔ ✔ ✔ ✔ The README also explains the keys: Read more...

Alibi Detect is an open source Python library (sister library to Alibi ) focused detecting outliers, adversarial examples, and concept drift. Finding adversarial examples is relevant for assessing the security of machine learning models. Machine learning models learn complex statistical patterns in datasets. If these statistical patterns “drift” (in unforeseen ways) after a model is deployed, this will decrease the model performance over time. In systems where model predictions have an impact on people, this may be a threat to the fairness of the predictions. Read more...

The Adversial Robustness Toolbox (ART) is the first comprehensive toolbox that unifies many defensive techniques for four categories of adversarial attacks on machine learning models. These categories are model evasion, model poisoning, model extraction and inference (e.g. inference of sensitive attributes in the training data; or determining whether an example was part of the training data). ART supports all popular machine learning frameworks, all data types and all machine learning tasks. Read more...

A data statement, according to the authors, is … a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize,how software might be appropriately deployed,and what biases might be reflected in systems built on the software. (587) This paper specifically focuses on ethically responsive NLP technology. The authors argue that a data statement should be an integral part of work and writing on NLP. Read more...

Word embeddings are a widely used representation for text data. A well-known example in natural language processing (NLP) is Word2vec, which uses a neural network to learn latent vector representations of words. It turns out that relations in this latent vector space capture semantic relations quite well. For example, by finding similar vectors you typically end up with highly related or synonymous words. Another typical example is that when you add up the vectors of “king” and “woman”, you end up with the vector corresponding to “queen”, so even a form of conceptual calculus is possible. Read more...

The DeepExplain Python package for TensorFlow models and Keras models with TensorFlow backend offers two types of interpretability methods for deep convolutional neural networks: gradient-based methods and perturbation-based methods. This package does not seem to be very actively maintained anymore and support for TensorFlow V2 is limited. Attributions The README gives the following clear and succinct explanation of what an “attribution” is. All methods included in this approach allow visualization of how each input feature contributes to the final prediction, in terms of what a particular targeted neuron “sees”: Read more...

A brief explanation of the gradient-based interpretability method called DeepLIFT is given by Shrikumar et al. in the abstract of the linked paper: DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its ‘reference activation’ and assigns contribution scores according to the difference. Read more...

ELI5 (“Explain Like I’m 5”) provides model-specific support for models from scikit-learn, lightning, decision tree ensembles using the xgboost, LightGBM, CatBoost libraries. ELI5 mainly provides convenient wrappers to couple the feature importance coefficients that these libraries already provide with feature names, as well as convenient ways to visualize importances, e.g. by highlighting words in a text. For Keras image classifiers an implementation of the gradient-based Grad-CAM visualizations is offered, but the TensorFlow V2 backend is not supported. Read more...

This handcrafted dataset can be used to evaluate bias in AI using text data for NLP tasks. Dataset description: Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems and resources. Further, there is a lack of benchmark datasets for examining inappropriate biases in system predictions. Here, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. Read more...

Interpret-Text is an extension of InterpretML , specifically for several text models. Three modules are provided: ClassicalTextExplainer, UnifiedInformationExplainer and IntrospectiveRationaleExplainer. Classical Text Explainer The ClassicalTextExplainer supports linear models from sklearn with a coefs_ call and tree-based models for which feature_importances_ is defined. ClassicalTextExplainer includes a NLP pipeline from preprocessing to hyperparameter tuning, so it accepts raw text data as input. The default pipeline uses a unigram bag-of-words model. Elements of the pipeline can be replaced if desired. Read more...

The InterpretML toolkit, developed at Microsoft, can be decomposed in two major components: A set of interpretable “glassbox” models Techniques for explaining black box systems. W.r.t. 1, InterpretML particularly contains a new interpretable “glassbox” model that combines Generalized Additive Models (GAMs) with machine learning techniques such as gradient boosted trees, called an Explainable Boosting Machine. Other than this new interpretable model, the main utility of InterpretML is to unify existing explainability techniques under a single API. Read more...

The type of explanation LIME offers is a surrogate model that approximates a black box prediction locally. The surrogate model is a sparse linear model, which means that the surrogate model is interpretable (in this case, it’s weights are meaningful). This simpler model can thus help to explain the black box prediction, assuming the local approximation is actually sufficiently representative. The intuition behind this is provided in the README: Intuitively, an explanation is a local linear approximation of the model’s behaviour. Read more...

The OpenMined community is a collaboration of several organizations, including TensorFlow, PyTorch and Keras, to create an open-source ecosystem of privacy tools that extend libraries such as PyTorch with cryptographic techniques and differential privacy. The aim is to contribute to the adaptation of privacy-preserving AI. To this end, OpenMined offers several privacy-preserving tools on their github. A main tool is PySyft, which allows “computing on data you do not own and cannot see”. Read more...

The SHAP package is built on the concept of a Shapley value and can generate explanations model-agnostically. So it only requires input and output values, not model internals: SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. (README) Additionally, this package also contains several model-specific implementations of Shapley values that are optimized for a particular machine learning model and sometimes even for a particular library. Read more...

TensorFlow Privacy is a library that allows you to replace default TensorFlow optimizers with optimizers that allow training with differential privacy, i.e. they implement forms of stochastic gradient descent (SGD) with differential privacy. Because large neural networks or other differentiable models have a very large learning capacity, it can happen that the model achieves high performance on uncommon training input by simply “memorizing” the training input. If the training data is sensitive, for example information about a specific user, this is undesired behavior that may leak private information. Read more...

The What-If Tool (WIT) takes a pretrained model and then allows you to visualize the effect of changing e.g. classification thresholds or the data points themselves on performance, explainability and fairness metrics. Many convenient functions for gaining insight in the data set are provided, such as binning on particular features, attribution values, or inference scores, computing partial dependence plots, and typical performance indicators such as a confusion matrix or ROC curve. Read more...