Demokratis.ch is a non-profit project working to modernise the consultation procedure—a key democratic process that allows Swiss citizens to provide feedback on proposed laws and amendments. Today, the process is slow and cumbersome for everyone involved: it requires studying lengthy PDFs, writing formal letters, and even synthesising legal arguments by copy-pasting into Excel. There’s a huge opportunity to streamline this process and make this democratic tool more accessible and inclusive. In this talk, I’ll share how we’re tackling this challenge with machine learning: building data processing pipelines, extracting features from endless PDFs, embedding and classifying text, designing and evaluating models—and ultimately deploying them in production. Because the data comes from the federal administration and 26 different cantons, it’s often heterogeneous and in varying formats. Data quality, in general, presents many challenges for both training and evaluation. Spoiler: PDF is a pretty terrible format for machines … Our approach is practical and pragmatic, and our code is open source, so you’re welcome to explore our solutions or even contribute yourself!

about this event: https://talks.python-summit.ch/sps25/talk/EWUJKH/