I, Robot. You, Journalist. Who is the Author?: Authorship, bylines and full disclosure in automated journalism
Abstract
The broadening reliance on algorithms to generate news automatically, referred to as “automated journalism” or “robot journalism”, has significant practical, sociopolitical, psychological, legal and occupational implications for news organizations, journalists and their audiences. One of its most controversial yet unexplored aspects is the algorithmic authorship. This paper integrates a multidisciplinary theoretical framework of algorithmic creativity, bylines and full disclosure policies, legal views on computer-generated works, and an empirical study of attribution regimes in pioneering organizations that produce journalistic content automatically. Fieldwork included quantitative content analysis of automated stories on 12 websites and interviews with key figures from seven of the organizations that agreed to be interviewed, despite the general reluctance of news organizations to be identified with such an endeavor. The study detects major discrepancies between the perceptions of authorship and crediting policy, the prevailing attribution regimes and the scholarly literature. To mitigate these discrepancies, we offer a consistent and comprehensive crediting policy that sponsors public interest in automated news.
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In an interview study published in 2017, members of news organizations did not perceive algorithms as authors.
“In an interview study published in 2017, members of news organizations did not perceive algorithms as authors [78].”
From The AI Ghostwriter Effect: When Users do not Perceive Ownership of AI-Generated Text but Self-Declare as Authors (2024) · cites this paper as [78]
Cites
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Flagged first, then by citations
Cited by
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“Montal and Re-ich [78] suggest using bylines of automatically generated articles to reference software vendors and a mention in a disclosure statement for algorithmic text parts.”
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- Authors
- Montal, T., & Reich, Z.
- Affiliations
- Ben-Gurion University of the Negev (IL)
- Corresponding author
- Montal, Tal
- Reich, Zvi
- ORCID
- Reich, Zvi
- Venue
- Digital Journalism
- Publisher
- Informa UK Limited
- Published
- 2016-08-05
- Volume
- 5
- Issue
- 7
- Pages
- 829-849
- ISSN
- 2167-0811 (ISSN-L)
- 2167-082X
- OpenAlex
- W2520168431
- Cited by
- 208
- References
- 2
- Citations by year
- 2026: 25 · 2025: 23 · 2024: 37 · 2023: 25 · 2022: 29 · 2021: 27 · 2020: 19 · 2019: 14 · 2018: 5 · 2017: 4
- FWCI
- 21.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Digital Journalism (publishedVersion)
- Topics
- Social Sciences › Communication › Journalism and Media Studies
- Social Sciences › Communication › Media Studies and Communication
- Social Sciences › Political Science and International Relations › Intellectual Property Rights and Media
- Keywords
- algorithmic authorship, automated reporting, attribution, bylines, computer-generated works, news organizations
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