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 parameterisation. This observation suggests that each use case may require a bespoke surrogate built and tuned for the problem at hand.

This session introduces the three core components of surrogate explainers: data sampling, interpretable representation and explanation generation in view of text, image and tabular data. By understanding these building blocks individually, as well as their interplay, we can build robust and trustworthy explainers. However, we can also misuse these insights to create technically-valid explainers that are intended to produce misleading justifications of individual predictions. For example, by manipulating the size and distribution of the data sample (or the grouping criteria of the interpretable representation) an automated decision may be shown as fair despite the underlying model being inherently biased. This overview of theory is complemented by a low-code hands-on exercise facilitated through an iPython widget delivered via a Jupyter Notebook.

Delivered by ADM+S Affiliate Kacper Sokol and ADM+S Associate Investigator Danula Hettiachchi

ADM+S Members Only – Registration and Zoom link via ADM+S Calendar invite