  The work covers the development and explainability of machine learning models
for predicting political leanings through parliamentary transcriptions. We
concentrate on the Slovenian parliament and the heated debate on the European
migrant crisis, with transcriptions from 2014 to 2020. We develop both
classical machine learning and transformer language models to predict the left-
or right-leaning of parliamentarians based on their given speeches on the topic
of migrants. With both types of models showing great predictive success, we
continue with explaining their decisions. Using explainability techniques, we
identify keywords and phrases that have the strongest influence in predicting
political leanings on the topic, with left-leaning parliamentarians using
concepts such as people and unity and speak about refugees, and right-leaning
parliamentarians using concepts such as nationality and focus more on illegal
migrants. This research is an example that understanding the reasoning behind
predictions can not just be beneficial for AI engineers to improve their
models, but it can also be helpful as a tool in the qualitative analysis steps
in interdisciplinary research.
