Critical Tools for Ethical and Explainable AI | by Nakul Upadhya | Jul, 2023

Photo by Wesley Tingey on Unsplash

A guide to essential Libraries and Toolkits that can help you create trustworthy yet robust models

Nakul Upadhya

Machine learning models have revolutionized numerous fields by delivering remarkable predictive capabilities. However, as these models become increasingly ubiquitous, the need to ensure fairness and interpretability has emerged as a critical concern. Building fair and transparent models is an ethical imperative for building trust, avoiding bias, and mitigating unintended consequences. Fortunately, Python offers a plethora of powerful tools and libraries that empower data scientists and machine learning practitioners to address these challenges head-on. In fact, the variety of tools and resources out there can make it daunting for data scientists and stakeholders to know which ones to use.

This article delves into fairness and interpretability by introducing a carefully curated selection of Python packages encompassing a wide range of interpretability tools. These tools enable researchers, developers, and stakeholders to gain deeper insights into model behaviour, understand the influence of features, and ensure fairness in their machine-learning endeavours.

Disclaimer: I will only focus on three different packages since these 3 contain a majority of the interpretability and fairness tools anyone may need. However, a list of honourable mentions can be found at the very end of the article.



Interpretable models play a pivotal role in machine learning, promoting trust by shedding light on their decision-making mechanisms. This transparency is crucial for regulatory compliance, ethical considerations, and gaining user acceptance. InterpretML [1] is an open-source package developed by Microsoft’s research team that incorporates many crucial machine-learning interpretability techniques in one library.

Post-Hoc Explanations

First, InterpretML includes many post-hoc explanation algorithms to shed light on the internals of black-box models. These include:

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