Predicting and Analyzing Law-Making in Kenya
This work addresses legislative prediction in a developing democracy, but it is incremental as it applies existing methods to a new dataset.
The paper tackled predicting bill enactment in Kenya's parliament using machine learning on bill features, finding that temporal and categorical features were more relevant than bill text, with specific accuracy metrics not provided.
Modelling and analyzing parliamentary legislation, roll-call votes and order of proceedings in developed countries has received significant attention in recent years. In this paper, we focused on understanding the bills introduced in a developing democracy, the Kenyan bicameral parliament. We developed and trained machine learning models on a combination of features extracted from the bills to predict the outcome - if a bill will be enacted or not. We observed that the texts in a bill are not as relevant as the year and month the bill was introduced and the category the bill belongs to.