FAT Forensic Events

An overview of machine learning explainability for the ADM+S Machines Programme

Learn more about FAT Forensics: Source Code Documentation

Resources: Slides Slack

Making Machine Learning Explanations Truthful and Intelligible

An interactive overview of machine learning explainability with focus on robustness of surrogate explainers for image and tabular data for the Machines Programme of the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S).
Where: (MS Teams) ADM+S Centre, RMIT University, Melbourne, Victoria, Australia.
When: 3.30–4.30pm (AEST) on Friday, August 13th, 2021.

Table of Contents

About the Invited 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 invited 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 of them. 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.

Resources

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.

Resources
Slides

Instructors

Kacper Sokol

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.

Contact
K.Sokol@bristol.ac.uk

References

  1. 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 ↩

  2. 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 ↩

  3. 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 ↩