Slow and Fast Journalism with Artificial Intelligence - A Theoretical Approach

Abstract

The literature on AI and journalism tends to present as the central issue that the AI-driven replacement of repetitive tasks in journalism could lead to the replacement of non-repetitive tasks that should be performed by humans.
Arguing for the continued role of humans in AI-supported news production tends to rests on the idea that humans create what is genuinely new rather than merely repeating patterns, and that they prioritise quality over quantity.
However, this distinction between, on the one hand, repetition and novelty and, on the other hand, quantity and quality, does not allow for an understanding of how AI can actually produce news and why this technology constitutes a real threat to human journalists, given its potential to replace them. In this theoretical analysis, the use of philosophy is proposed to think about the relationship between repetition and the new, as well as between the quantitative and the qualitative, in order to defend a speculative model of "slow AI journalism" that could enhance the quality of journalism, provide a sustainable business model in the era of AI, and improve current AI models.