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.

本文的研究主要是关于使用机器学习预测政治倾向，并采用 Slovenian 议会的演讲记录，通过传统的机器学习和转换语言模型对左右倾向进行预测，并运用可解释性技术识别关键字短语来解释决策。

计算语言学中的 XAI：理解斯洛文尼亚议会中的政治倾向

XAI in Computational Linguistics: Understanding Political Leanings in  the Slovenian Parliament

In recent years, a rising numbers of people arrived in the European Union,
traveling across the Mediterranean Sea or overland through Southeast Europe in
what has been later named as the European migrant crisis. In the last 5 years,
more than 16 thousands people have lost their lives in the Mediterranean sea
during the crossing. The United Nations Secretary General Strategy on New
Technologies is supporting the use of Artificial Intelligence (AI) and Robotics
to accelerate the achievement of the 2030 Sustainable Development Agenda, which
includes safe and regular migration processes among the others. In the same
spirit, the central idea of this project aims at using AI technology for Search
And Rescue (SAR) operations at sea. In particular, we propose an autonomous
fleet of self-organizing intelligent drones that would enable the coverage of a
broader area, speeding-up the search processes and finally increasing the
efficiency and effectiveness of migrants rescue operations.

该研究项目旨在使用人工智能技术和自主系统组织的智能无人机，以加速在欧洲移民潮中海上的搜索和拯救操作，提高拯救行动的效率和有效性。