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Explainable AI: 5 Popular Frameworks To Explain Your Models

Artificial Intelligence is fiercely marching ahead in the technological arena and enabling developers and businesses to build the finest army of models and tools with its state-of-the-art algorithms. The evolution of artificial intelligence has been enormously intriguing and dynamic in equal parts.

We have seen the rise of some advanced models in various forms like machine learning, and deep learning to combat more challenging issues. Another branch of artificial intelligence that is creating quite an impact across various industries is explainable AI or XAI.

According to an IBM study, it was found that ‘building trustworthy Artificial Intelligence (AI) is perceived as a strategic differentiator and organizations are beginning to implement AI ethics mechanisms.’

  • 80% of respondents iterated that a non-technical executive was the primary advocate for AI ethics, compared to 15% in 2018, the study reported.
  • 79% of CEOs surveyed did seem prepared to put AI ethics into practice but only less than a quarter of organizations implemented it.

In this article, we will be putting together a list of popular python libraries that your models can count on. Before we dive ahead, we are deciphering the concept of Explainable AI.

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What is Explainable AI?

Explainable AI is a sub-part of artificial intelligence which enables businesses to troubleshoot and enhance the model performance and advance the practice of honest and trustworthy AI. In recent times, explainable AI has shown immense potential with continuous innovation and the introduction of newer and more elegant techniques.

To explain it in layman’s language, Explainable AI can be defined as a compilation of different methods and processes that enable human users to understand the predictions made by their machine learning models. It is primarily used to describe an AI model, forecast its expected impact and point out its potential biases.

Typically, Explainable AI takes care of the accuracy, transparency, and fairness in the process of AI-enabled decision-making.

Why Explainable AI?

In the current times, technology is changing every minute and has become very intricate, sometimes; humans are unable to comprehend the process of how an algorithm gave a certain output. Here, the calculation is referred to as a “black box” meaning impossible to interpret.

Typically, these models are the direct outcome of data and often, engineers/data scientists who are responsible for writing the algorithm come into a fix. They can neither explain what’s happening inside the model nor can they ascertain how the AI algorithm concludes the particular result.

However, the biggest advantage of explainable AI is that it helps the developers to understand how the AI-powered system came to that outcome.

Though explainable AI does streamline the model to a great extent, the business must have a thorough knowledge of AI decision-making processes and not blindly rely on them. It can enable humans to decipher deep learning, neural networks, and machine learning algorithms.

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To implement responsible AI in all fairness and accountability, organizations must understand that explainable AI is a vital element. Businesses must look forward to embedding ethical principles into AI processes by creating AI systems based on transparency.

With Explainable AI, users can get a holistic view of questions like why this AI system makes this prediction. Why did the AI model take this particular decision? Why did the Ai system fail its prediction? How can the system troubleshoot?

Peter Bernard, CEO of Datagration, asserted that it’s good for companies to understand AI but what gives them an edge over other organizations is the implementation of explainable AI that enables businesses to optimize their data.

He further added,

“Not only are they able to explain and understand the AI/ML behind predictions, but when errors arise, they can understand where to go back and make improvements. A deeper understanding of AI/ML allows businesses to know whether their AI/ML is making valuable predictions or whether they should be improved.”

How Explainable AI Helps Businesses

  • Troubleshoot and improve model performance.
  • Enable stakeholders to have a clear picture of the behaviors of AI models.
  • Investigate model behavior with the help of model insights.
  • Constant tracking enables organizations to compare model predictions and improve performance.

If the concept of explainable AI is clear, let’s focus on 5 essential explainable AI python frameworks that can help users to implement explainable AI.


Local Interpretable Model-Agnostic Explanations, also known as LIME, is a combination of model-agnosticism and local explanation techniques. Model-agnosticism is LIME’s property which enables it to provide explanations for any specific supervised learning model by simply considering it as a black box model. Local explanations indicate that the explanations provided by LIME are locally faithful and in tune with the vicinity of the said sample.  LIME works smoothly, though in a limited fashion, with machine learning and deep learning models, it is still considered among the most common XAI methods.

What happens when LIME is given a dataset and a prediction model? To begin with, LIME provides locally faithful explanations and then it produces 5000 samples (by default). With the help of the prediction model, it retrieves the target variable for the 5000 samples. After getting the surrogate dataset, LIME examines each row to understand how close they are to the original and then gets to the top features using techniques like Lasso.

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In 2017, Scott M Lundberg and Su-In Lee’s SHAP original algorithm was published and since then it has been widely accepted and adopted in various fields. The paper focused on why a model made a particular prediction, and how it could be as vital as the accuracy of the prediction. They also stated that optimal levels of accuracy are only achieved with complex models. Surprisingly, even the finest minds struggle to interpret these models (deep learning or ensemble), creating a tension between accuracy and interpretability.

To address this problem, Scott and Su-In presented a unified framework for interpreting predictions, SHAP (Shapley Additive exPlanations).

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SHAP also referred to as Shapley’s Additive explanations use a game theoretic approach to predict the outcome of any machine learning model. With the help of classic shapely values, SHAP connects optimal credit allocation with local explanations. SHAP’s novel components include:

  • The identification of a new class of additive feature importance measures, and
  • Theoretical results show there is a unique solution in this class with a set of desirable properties.


Designed for an explainable AI pipeline, Eli5 is a python toolkit that empowers users to debug different machine-learning models with the help of a uniform API. Besides being capable of describing blackbox models, Eli5 also comes with built-in support for several ML frameworks and packages. We have listed a few of them below:


  • ELI5 is enabled to explain weights and predictions of sci-kit-learn linear classifiers and regressors.
  • Print decision trees as text or as SVG.
  • Depict feature importances.
  • Explain decision tree predictions and tree-based ensembles.
  • Debug sci-kit-learn pipelines which contain HashingVectorizer.
  • Highlight text data.


  • Explains predictions of image classifiers with the help of Grad-CAM visualizations.


  • Show feature importances.
  • Explain predictions of XGBRegressor, xgboost.Booster and XGBClassifier.


  • Show feature importances.
  • Explain predictions of LGBMRegressor and LGBMClassifier.


  • Show feature importances of CatBoostClassifier and CatBoostRegressor.


  • Explain weights and predictions of lightning classifiers and regressors.


  • Check weights of sklearn_crfsuite.CRF models.

Eli5 incorporates different algorithms to analyze blackbox models.

  • The TextExplainer: It lets the user explain of predictions of any text classifier using the LIME algorithm (Ribeiro et al., 2016). in addition, users can opt for utilities to use LIME with arbitrary black-box classifiers and non-text data. This feature is a work in progress.
  • Permutation Importance method: It is used to compute feature importances for black box estimators.


This python library understands the value of engaging visuals and interactivity while presenting insights, data stories, and model results. Technically, combining visuals and interactivity into a web app indicates how data scientists and other businesses should interact with ML outcomes in the future and SHapash has already set a course for it.

Created by data scientists of a French insurer, MAIF, the package consists of an array of visualizations around SHAP/Lime explainability. Shapash publishes an interactive dashboard as a web app besides relying on SHAP or LIME to assess contributions. It is dependent on the various mandatory steps to build an ML model and for the results to be comprehensible.

SHapash works seamlessly with multiclass problems, regression, and binary classifications and is known to be compatible with models such as Sklearn Ensemble, LightGBM, SVM, Catboost, Xgboost, and Linear models.


Dalex is a popular open-source library known for providing wrappers around numerous ML frameworks. Users can not only explore these wrappers around models but also compare them with local and global explainers’ collections. This library is a part of DrWhy.AI, an ebook that delves into the philosophical and methodological details of Dalex.

The biggest advantage of Dalex is that it can scan through any model and enables its users to explain the behavior and highlights how complex models work.

The minds behind the DALEX framework wanted the ML model users to understand either the feature attributions or variables of the final prediction in addition to comprehending the sensitivity of particular features. They also wanted the users to double-check the strength of the evidence that confirmed a specific prediction.

Available in both R and Python, this package includes Breakdown & SHAP waterfall plots, variable importance, and PDP & ALE plots. Dalex also has a wrapper around the native SHAP package in python. It is compatible with a host of ML frameworks like xgboost, keras, mlr3, mlr, scikit -learn, H2O, and tidymodels.

  • Dalex uses Arena, an interactive drag-and-drop dashboard.
  • Plots are interactive.
  • They have a neat integration with Plotly.

When we speak of governance of AI tools and systems, needless to say, it takes more than one leader, and one organization to ignite the lamp and take the first steps into AI regulation. If industry leaders decide to lead the change, the future of explainable definitely looks promising.

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