From normative principles to epistemic governance: A workflow-based analysis of European journalism ethics in datafied environments

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Data journalism has received limited attention in journalism ethics, where it is often assumed that established normative principles apply directly to data-driven practices. However, data journalism relies on complex data processes that introduce distinct epistemic challenges related to bias, interpretation, and accountability. This study examines data journalism ethics as a form of epistemic governance embedded across key stages of the data journalism workflow, from data gathering to data visualisation, drawing on a thematic analysis of 37 ethical codes from 32 European countries. The findings show that, while ethical codes consistently articulate principles such as accuracy, fairness, and transparency, they rarely operationalise them in relation to methodological decisions shaping data-driven reporting. The epistemic governance framework serves as a bridge between data journalism ethics and AI ethics, as many AI-related ethical challenges originate in data practices and are intensified by automation.