# What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components

arXiv: https://arxiv.org/abs/2209.03813 · HTML: https://mlsanity.com/paper/2209.03813/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [bLIMEy: Surrogate Prediction Explanations Beyond LIME](https://mlsanity.com/paper/1910.13016/) (2019)
- [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021)
- [FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency](https://mlsanity.com/paper/1909.05167/) (2019)
- [You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source](https://mlsanity.com/paper/2107.06639/) (2021)

## Built on by (most cited first, 3 of 3)

- [Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication](https://mlsanity.com/paper/2302.03460/) (2023)
- [What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks](https://mlsanity.com/paper/2403.12730/) (2024)
- [Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations](https://mlsanity.com/paper/2303.00934/) (2023)

## Abstract (arXiv)

Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such systems and help to hold them accountable. New transparency approaches are developed at breakneck speed, enabling us to peek inside these black boxes and interpret their decisions. Many of these techniques are introduced as monolithic tools, giving the impression of one-size-fits-all and end-to-end algorithms with limited customisability. Nevertheless, such approaches are often composed of multiple interchangeable modules that need to be tuned to the problem at hand to produce meaningful explanations. This paper introduces a collection of hands-on training materials -- slides, video recordings and Jupyter Notebooks -- that provide guidance through the process of building and evaluating bespoke modular surrogate explainers for tabular data. These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation.