How (not to) Run an AI Project in Investigative Journalism

Abstract

Data journalists are increasingly reliant on automation and artificial intelligence (AI) to process and analyse massive datasets. AI can contribute to journalism by creating visualizations, verifying accuracy of information, analysing historical data, monitoring social media, finding patterns and outliers, generating text and much more. However, the integration of AI into the newsroom comes with its own challenges. In this article, we take a practice-based approach to develop a deeper understanding of how to overcome such challenges. Our teams of data scientists, AI experts and journalists took on four projects incorporating data science and machine learning into investigative journalism. From those experiences, we found that access to data at scale, data quality and reworking the concept of “newsworthy” as a machine learning question were the most significant obstacles to deploying AI in the newsroom. We recommend closer collaborations between team members of different disciplines to create a truly trans-disciplinary approach, as well as some practical considerations for choosing projects to facilitate successful AI-assisted investigations.