Do You Trust Your Explainer?
|An interactive overview of machine learning explainability with focus on robustness of surrogate explainers for image and tabular data for a workshop organised by Workpackage 3 of the TAILOR project.|
|When:||10.00–10.30am on Thursday, September 2nd, 2021.|
Table of Contents
About the Talk
Surrogate explainability is a popular transparency technique for assessing trustworthiness of predictions output by black-box machine learning models. While such explainers are often presented as monolithic, end-to-end tools, they in fact exhibit high modularity and scope for parameterisation1. This observation suggests that each use case may require a bespoke surrogate built and tuned for the problem at hand. This talk provides an introduction to interpretable machine learning and explainable artificial intelligence, and overviews the influence of parameterisation and configuration of surrogates on the explanations that they generate for tabular and image data. In particular, it discusses the influence of segmentation granularity and super-pixel occlusion colour for images, as well as discretisation and binarisation of continuous features for tabular data. Understanding these dependencies helps with building robust and trustworthy surrogate explainers, whose insights can be relied upon.
FAT Forensics (Software)
To support the goals of this talk, we employ
FAT Forensics – an open source Python package
that can inspect selected fairness, accountability and transparency aspects
of data (and their features), models and predictions.
The toolbox spans all of the FAT domains because many of them share underlying
algorithmic components that can be reused in multiple different
implementations, often across the FAT borders.
This interoperability allows, for example, a counterfactual data point
generator to be used as a post-hoc explainer of black-box predictions on
one hand, and as an individual fairness (disparate treatment) inspection tool
on the other.
The modular architecture23 enables
FAT Forensics to deliver robust and tested
low-level FAT building blocks as well as a collection of FAT tools built on top
Users can choose from these ready-made tools or, alternatively, combine the
available building blocks to create their own bespoke algorithms without the
need of modifying the code base.
The presentation is provided as an interactive set of slides utilising iPyWidgets and built with a Jupyter Notebook based on RISE and reveal.js. This notebook (hence the presentation) can be executed locally on one’s own machine or launched directly in the web browser through Google Colab or MyBidner.
Kacper is a research associate with the TAILOR project at
the University of Bristol.
His main research focus is transparency – interpretability and
explainability – of machine learning systems.
In particular, he has done work on enhancing transparency of logical predictive
models (and their ensembles) with counterfactual explanations.
Kacper is the designer and lead developer of the
FAT Forensics package.
Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez, and Peter Flach. 2019. bLIMEy: Surrogate Prediction Explanations Beyond LIME. 2019 Workshop on Human-Centric Machine Learning (HCML 2019) at the 33rd Conference on NeuralInformation Processing Systems (NeurIPS 2019), Vancouver, Canada (2019). https://arxiv.org/abs/1910.13016 ↩
Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raul Santos-Rodriguez, and Peter Flach. 2020. FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. Journal of Open Source Software, 5(49), p.1904. https://joss.theoj.org/papers/10.21105/joss.01904 ↩
Kacper Sokol, Raul Santos-Rodriguez, and Peter Flach. 2019. FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency. arXiv preprint arXiv:1909.05167. https://arxiv.org/abs/1909.05167 ↩