News automation can be used as a final product that will be delivered to the audiences without any journalistic mediation or as a first draft that journalists will enrich with their expertise. These recommendations aim to provide guidelines to promote ethical practices, considering that news information is a public good that commits all stakeholders to the audiences.

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 systems accountable, less attention was paid to data quality, while the results’ accuracy and efficiency depend on high-quality data in any machine learning task.

In machine learning, fairness is defined as the absence of bias and equal treatment. Hence, it can be approached through biased datasets and outcomes, where a dominant group is favoured to the detriment of others. Biases can be related to the training data or the implicit human values of the people involved in the programming.