Advbox offers a number of AI model security toolkits. AdversialBox allows zero-coding generation of adversial examples for a wide range of neural network frameworks. An overview of the supported attacks and defenses can be found here and the corresponding code here. It requires some effort to find all attacks mentioned on the homepage in the code base. Generally speaking, the documentation of AdvBox is incomplete and not very user-friendly. ODD: Object Detector Deception showcases a specific attack for object detection networks such as YOLO, but is not mentioned in the README. Read more...

Butnaru and others associated with the HAI center at Stanford set up a Agile Ethics workflow in the form of a Trello board. From left to right, the workflow walks you through relevant ethical considerations at the various steps of a machine learning pipeline. The phases are: Scope Consider ethical implications of the project Consider skill mapping (what’s the impact of AI on jobs)? Facilitates up-skilling or a change of strategy in the use of human talent Data audit Led by Chief Data Officer “Meet and plan” stage in Agile Helpful: Data Ethics Canvas Train Build stage in Agile Consider (tools for) transparency and fairness Analyse Benchmarks, including benchmarks related to e. Read more...

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...

The IBM AI Fairness 360 Toolkit contains several bias mitigation algorithms that are applicable to various stages of the machine learning pipeline. The toolkit implements different notions of fairness, both on individual and the group level, and several fairness metrics for both classes of fairness. The toolkit provides additional guidance on choosing metrics and mitigation algorithms given a particular goal and application. The following should be noted when using the fairness toolkit (and other similar toolkits, for that matter): 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...

Captum is a model interpretability library specifically PyTorch. It is actively maintained at the moment of writing and supports an extensive array of interpretability methods. The Captum website also offers a large range of hands-on tutorials for various use cases. Supported interpretability methods Captum supports a very extensive list of interpretability algorithms. All paper references for each of the supported methods are listed in the README, so they will not be repeated here. Read more...

CleverHans is a Python library with the main purpose of providing good reference implementations of attacks for benchmarking machine learning models against adversarial examples. The main maintainers of this library are Ian Goodfellow and Nicolas Papernot. Attacks (i.e. methods for generating adversarial examples) are listed under /cleverhans and each of the supported frameworks has its own folder with attack implementations. CleverHans also aims to implement a set of defenses, but this is currently work in progress (currently there is only a defense implementation for PyTorch). Read more...

The Data Ethics Canvas is a tool developed by the Open Data Institute for providing ethical guidance to organizations doing any type of project involving data. That includes data collection, sharing, and its usage for example in machine learning applications. The tool is accompanied with a white paper and a brief practical guide for its usage. Page 3 of the practical guide lists some recommendations that are also relevant when you do not use this tool. 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...

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...

The documentation of fairlearn is excellent and provides a good introduction to the topic of fairness in AI. It is emphasized that fairness algorithms are no plug-and-play technical solutions, but require serious thought about the context of the data and the problem at hand. Fairness is a fundamentally sociotechnical challenge and cannot be solved with technical tools alone. They may be helpful for certain tasks such as assessing unfairness through various metrics, or to mitigate observed unfairness when training a model. Read more...

The not-so originally named “fairness in classification” provides a Python implementation of three fairness constraints for logistic regression: Disparate impact: similar acceptance rate for different demographic groups. See Zafar et al., 2017 a. Disparate mistreatment: similar misclassification rate for different demographic groups. See Zafar et al., 2017b Preference-based fairness (as opposed to parity-based fairness): a more game-theoretical approach where decision boundaries are chosen such that it can be shown that each group prefers its own decision boundary, if rational. Read more...

This repository by H2O.ai contains useful resources and notebooks that showcase well-known machine learning interpretability techniques. The examples use the h2o Python package with their own estimators (e.g. their own fork of XGBoost), but all code is open-source and the examples are still illustrative of the interpretability techniques. These case studies that also deal with practical coding issues and preprocessing steps, e.g. that LIME can be unstable when there are strong correlations between input variables. 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 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...

Introduction A major downside of external auditing is that it typically only can be done after model deployment. This paper presents a methodology for internal algorithmic auditing as an integral part of the development process, end-to-end. Those who move fast and break things, beware: The audit process is necessarily boring, slow, meticulous and methodical—antithetical to the typical rapid development pace for AI technology. However, it is critical to slow down as algorithms continue to be deployed in increasingly high-stakes domains. 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...

This library provides a separate predict() function for scikit-learn tree-based models (so also ensembles) that outputs a prediction with interpretable elements of the shape prediction = bias + feature_1_contribution + ... + feature_n_contribution. That is, it turns these tree-based models into a white box , where we can inspect how much each feature contributes to the predicted value (in the case of regression) or how much it contributes to the estimated probability of a class (given classification). Read more...