Dataharvest 2026 - the European Investigative Journalism Conference

Processing natural language is seen as the task that artificial intelligence is most adept at. However, as journalists and researchers, we need our technologies to be explainable, understandable, and deterministic. Because of this, not all artificial intelligence algorithms are well-suited for our work. And, when every company promises that their AI software is extraordinary, it's difficult to distinguish the empty promises from what the technology can actually do. Working on OpenAleph, an open-source tool for investigative journalism, has taught us a lot about processing natural language. We extract names of people and companies from raw text. We try to infer the language a text is written in. The names of places, cities, and countries are crucial to us, in order to situate data geographically. All of this is heavily reliant on algorithms. But not all algorithms are as good as getting us what we want!

In this session, we'll show you what works and what doesn't. Everything we demonstrate can be used independently of OpenAleph, and integrated into your own workflows. Some machine learning algorithms are excellent at getting us more insights from our data. In addition to this, data that we already have, or public data, can be harnessed to help us identify names of people and places, just based on similarity - no AI required!

Finally, we'll discuss how these approaches compare to using large language models and generative AI. This session is half teaching and discussing common solutions, half workshop. For the workshop part, bring a laptop running Python if possible.