Abstract
AI-driven journalism refers to various methods and tools for gathering, verifying, producing, and distributing news information. Their potential is to extend human capabilities and create new forms of augmented journalism. Although scholars agreed on the necessity to embed journalistic values in these systems to make AI-driven systems accountable, less attention is paid to data quality, while the results' accuracy and efficiency depend on high-quality data. However, data quality remains complex to define insofar as it is a multidimensional, highly domain-dependent concept. Assessing data quality in the context of AI-driven journalism requires a broader and interdisciplinary approach, relying on the challenges of data quality in machine learning and the ethical challenges of using machine learning in journalism. These considerations ground a conceptual data quality assessment framework that aims to support the collection and pre-processing stages in machine learning. It aims to contribute to strengthening data literacy in journalism and to make a bridge between scientific disciplines that should be viewed through the lenses of their complementarity.
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