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Human or machine? The perception of artificial intelligence in journalism, its socio-economic conditions, and technological developments toward the digital future

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  • Moravec, Vaclav
  • Hynek, Nik
  • Skare, Marinko
  • Gavurova, Beata
  • Kubak, Matus

Abstract

This study surveyed 1041 people in the Czech Republic to determine how well they could differentiate between news articles created by humans and those created by artificial intelligence (AI). It also explored attitudes toward AI-generated audio recordings and the future of journalism with AI. The study found that gender, age, and socioeconomic status were significant factors in how well respondents recognized the source of the text. Females were better at identifying human-generated texts, while males at identifying AI-generated texts. Younger respondents were generally more adept at recognizing AI-generated texts, education and income levels were also found to be correlated with better accuracy. Attitudes toward AI in journalism varied with age, with the 18–29 age group displaying ambivalence, the 30–49 age group being uncertain, the 50–69 age group having diverse attitudes, and the 70+ age group being skeptical. Males were more optimistic about AI's potential in journalism than females, especially among older age groups. The study's findings highlight the need for targeted digital literacy interventions tailored to different demographic groups. It provides insights into the development of digital literacy and the readiness of the population to use automated information outputs. This is essential to address the challenges of future technological development.

Suggested Citation

  • Moravec, Vaclav & Hynek, Nik & Skare, Marinko & Gavurova, Beata & Kubak, Matus, 2024. "Human or machine? The perception of artificial intelligence in journalism, its socio-economic conditions, and technological developments toward the digital future," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523008478
    DOI: 10.1016/j.techfore.2023.123162
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    References listed on IDEAS

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