Despite AI's promise, slow journalism practitioners emphasize the irreplaceable value of human creativity and emotional depth.
Research: Artificial Intelligence in Slow Journalism: Journalists’ Uses, Perceptions, and Attitudes. Image Credit: Shutterstock AI
Research: Artificial Intelligence in Slow Journalism: Journalists’ Uses, Perceptions, and Attitudes. Image Credit: Shutterstock AI
The term "slow journalism" was first coined by Susan Greenberg in 2007, highlighting the balance of narrative and investigative rigor, and later featured in the Oxford Dictionary of Journalism.
In a research paper published in the journal Journalism and Media, researchers at the Complutense University of Madrid, Spain, explored using artificial intelligence (AI) in slow journalism through 21 semi-structured interviews with practitioners in Spain.
The findings revealed that while interviewees made limited use of AI, they were generally indifferent or cautious about its potential, citing concerns over ethics and job loss. The study highlighted significant skepticism about AI's ability to replace or enhance the deeply human aspects of slow journalism, such as emotional engagement and narrative depth. They emphasized the importance of human responsibility in producing high-quality, creative stories. It was concluded that AI's current capabilities offer minimal enhancement to slow journalism, primarily helping with repetitive tasks.
Background
Past work has explored the intersection of AI and journalism, highlighting its efficiency gains and ethical challenges. While AI aids in content creation, it faces skepticism in enhancing long-form journalism, which relies on human creativity, emotional depth, and nuanced context. Studies have questioned whether AI can replace or augment journalism, especially in slow journalism's pursuit of quality. Cultural and ethical dimensions, such as the potential erosion of journalistic integrity, have been central to these debates.
Qualitative Research Method
Slow journalism emphasizes narrative craft and factual discovery, prioritizing storytelling that takes time to uncover overlooked stories and communicate them with high standards.
This research employed a qualitative method, utilizing semi-structured interviews to explore the perspectives of slow journalism practitioners. The aim was to gather in-depth insights into their opinions, feelings, and experiences.
Purposive sampling was applied to identify representative slow journalism publications in Spain, such as El País Semanal, Jot Down, and Libros del K.O., using criteria for selecting publications that prioritize investigative and narrative journalism, focusing on slow, quality journalism over fast-paced reporting.
The interviewees were selected based on three criteria: their roles in the chosen publications, the periodicity of the media, and an effort to ensure gender parity.
Dynamic questions were developed for the interviews; some were shared with all participants, and others were adapted to specific roles. The questions explored topics such as the use of AI tools in journalism and their impact on the work of slow journalists. Interviews were recorded, transcribed using Converterapp software, and analyzed by manually coding the responses into thematic patterns.
AI Adoption in Slow Journalism
Researchers interviewed 21 slow journalism practitioners, revealing varying levels of AI adoption. Of these, 11 reported using AI tools in their work, with leaders (L) and contributors (C) more inclined to adopt AI compared to staff editors (S). This variability reflects differences in openness to technology and perceived relevance to their roles.
Slow journalism critiques mainstream media's reliance on quick, surface-level reporting and promotes formats like long-form articles and literary journalism for a more reflective approach.
The most common AI applications were for tasks like transcribing interviews (8 out of 11) and translating texts (4 out of 11). However, many practitioners expressed caution regarding the integration of AI, acknowledging its potential but emphasizing its current limitations in reliability and quality.
Despite using AI for these tasks, respondents remained hesitant to use AI for more complex journalistic activities like writing in-depth reports or slow journalism pieces, which rely heavily on human observation and creativity. Several interviewees noted that AI's outputs often require substantial human review to meet quality standards.
While a few practitioners noted time savings in tasks like transcription, most agreed that AI has not yet significantly changed their journalistic practices. For instance, while transcription tools were appreciated for reducing labor, some practitioners preferred manual transcription to preserve context and sensory details, such as background sounds.
Many interviewees believed AI tools would be integrated into journalism in the future but were yet to be central to their work. Two leaders emphasized the importance of creating regulations around AI usage in newsrooms, suggesting that AI would be a supplementary tool rather than replacing human work. Practitioners were also mindful of the errors in AI-generated work, which limited its widespread use.
These attitudes align with a broader sense of ambivalence, where journalists recognize AI's potential but remain wary of its implications for quality and ethics. Most practitioners were skeptical about AI’s ability to replace the human aspects of slow journalism.
Some outlets, such as The Guardian’s The Long Read section, demonstrate how mainstream media can integrate slow journalism principles into their platforms.
Key elements like in-depth storytelling, human connection, and emotional resonance were seen as beyond AI’s current capabilities. While some acknowledged that AI could assist with repetitive tasks, they emphasized that AI could not replicate the essence of slow journalism, which involves original, creative work and human intuition.
Several respondents voiced concerns about the potential for AI to replace narrative journalism in the future. Still, they remained confident that a human touch would always be necessary for high-quality, creative reporting.
Cultural nuances also surfaced, with some respondents expressing concerns about the implications of using AI tools developed outside their local context.
While AI is used in specific tasks within slow journalism, its integration remains cautious. Many professionals view it as a supplementary tool rather than a game-changer. The consensus suggests that while AI could evolve to play a more significant role in the future, it is unlikely to replace the core aspects of slow journalism, particularly its emphasis on human creativity and emotional engagement.
Conclusion
To sum up, the study extended existing research on AI integration in journalism by analyzing the experiences and opinions of slow journalism practitioners in Spain. It found that while over half of the respondents used AI to transcribe interviews and translate texts, they expressed skepticism about its ability to improve quality or handle creative tasks.
AI was considered a supplementary tool, useful for routine work but unlikely to replace the human qualities essential to slow journalism. This finding aligns with earlier research on the limits of automation in creative fields.
The study highlighted concerns about AI’s limited impact on productivity and job security, suggesting that it would not significantly disrupt slow journalism jobs shortly. However, the research had limitations, such as a small sample size and a focus on Spain. Future research could explore how cultural differences shape AI adoption and its implications for journalism ethics.
Journal reference:
(2024). Artificial Intelligence in Slow Journalism: Journalists’ Uses, Perceptions, and Attitudes. Journalism and Media, 5(4), 1836-1850. DOI:10.3390/journalmedia5040111, https://www.mdpi.com/2673-5172/5/4/111