Abstract:
The interpretability of models has been one of the focal research topics in the machine
learning community due to a rise in the use of black box models and complex
state-of-the-art models [6]. Most of these models are debugged through trial and error,
based on end-to-end learning [7, 48]. This creates some uneasiness and distrust among
the end-user consumers of the models, which has resulted in limited use of black box
models in disciplines where explainability is required [33]. However, alternative models,
”white-box models,” come with a trade-off of accuracy and predictive power [7]. This research
focuses on interpretability in natural language processing for misinformation data.
First, we explore example-based techniques through prototype selection to determine if
we can observe any key behavioural insights from a misinformation dataset. We use
four prototype selection techniques: Clustering, Set Cover, MMD-critic, and Influential
examples. We analyse the quality of each technique’s prototype set and use two prototype
sets that have the optimal quality to further process for word analysis, linguistic
characteristics, and together with the LIME technique for interpretability. Secondly, we
compare if there are any critical insights in the South African disinformation context.
