Articial Intelligence and Journalism: exploring journalists’
perspectives
Inteligencia Articial y Periodismo: explorando el punto de vista de los periodistas
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How to cite this article:
Martínez-Navarro, G. (2025). Articial Intelligence and Journalism: exploring journalists’ perspectives.
Doxa Comunicación
, 40, pp. 259-278.
https://doi.org/10.31921/doxacom.n40a2717
Gema Martínez-Navarro.
A Lecturer at the Complutense University of Madrid (Marketing Department), she holds a
PhD in Economic and Business Sciences (specialising in Marketing) from the UCM. She has received the Diploma of
Teaching Excellence on two occasions and got a positive evaluation for her six-year research period (2020 call). She is
currently a full-time member of the Research Group “Responsible Communication and Vulnerable Publics” (931571 –
ComR) at the Faculty of Information Sciences (UCM). She has participated in several research teams funded by public
and private companies, carrying out studies focused on the eld of communication, new technologies and marketing; as
well as in projects on innovations in teaching of new technologies and university teaching. She works on numerous lines
of research, principally behaviour in the digital eld. She regularly participates in national and international conferences
and has published several papers in high-impact journals in the eld of communication. She is the author of the book
Fashion Marketing and Communication
by Esic Ed., and co-author of chapters in a number of books.
Complutense University of Madrid, Spain
[email protected]
ORCID:
0000-0002-9047-8010
is content is published under Creative Commons Attribution Non-Commercial License. International License CC BY-NC 4.0
Recibido: 24/06/2024 - Aceptado: 25/09/2024 - En edición: 16/10/2024 - Publicado: 01/01/2025
Received: 24/06/2024 - Accepted: 25/09/2024 - Early access: 16/10/2024 - Published: 01/01/2025
Resumen:
Numerosas investigaciones recientes han explorado el impacto de la inte
-
ligencia articial (IA) en el periodismo, presentando diferentes perspec
-
tivas. Sin embargo, se observa una carencia de estudios que investiguen
cómo los propios periodistas perciben y se adaptan a esta tecnología en su
trabajo cotidiano. Este artículo examina, desde la perspectiva de los pro
-
fesionales del periodismo, el impacto de la IA en sus labores diarias, inclu
-
yendo oportunidades, desafíos, implicaciones laborales y éticas. Utilizan
-
do una metodología mixta que combina revisión bibliográca y análisis
cualitativo mediante entrevistas, se obtiene una visión amplia y profunda
del fenómeno. Los hallazgos revelan dos actitudes predominantes: acep
-
tación y resistencia, condicionadas por el temor a la deshumanización
Abstract:
Numerous recent studies have explored the impact of articial
intelligence (AI) on journalism, from several dierent perspectives.
However, there is a dearth of studies that look into how journalists
themselves perceive and adapt to this technology in their daily
work. is paper examines, from the perspective of professional
journalists, the eect of AI on their daily tasks, including
opportunities, challenges, and the implications for work and ethics.
Mixed methodology has been employed combining a bibliographic
review and qualitative analysis through interviews to give a broad
and in-depth view of the phenomenon. e ndings reveal two
predominant attitudes: acceptance and resistance, conditioned by
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del ocio y la pérdida de competencias esenciales. No obstante, se subraya
la necesidad de integrar la IA de manera crítica y consciente, ya que es
indispensable en el trabajo periodístico contemporáneo para asegurar la
calidad e integridad del contenido informativo, así como preservar el pa
-
pel fundamental de los periodistas.
Palabras clave:
Inteligencia Articial; periodismo; automatización; fact-checking; ética.
fear of the dehumanisation of the profession and the loss of essential
skills. However, the need to integrate AI critically and consciously is
highlighted, since it is indispensable in contemporary journalistic
work to ensure the quality and integrity of news content, as well as to
preserve the fundamental role of journalists.
Keywords:
Articial Intelligence; journalism; automation; fact-checking; ethics.
1. Introduction
e technological revolution in the 21st century has transformed every sector and profession, and journalism is no exception.
Articial Intelligence (AI), dened as the ability of a machine to perform tasks that normally require human intelligence,
such as learning, reasoning, perception, and interaction with the environment (Russell & Norvig, 2020), is reshaping the
media landscape in profound and multifaceted ways. From automating news generation to fact-checking, AI has begun to
integrate processes that were previously exclusive to human creativity and judgment.
e application of AI to journalism has gone through numerous technological developments, starting in the 60s with
computer-assisted news writing, to the current “automated journalism” that uses AI to process large volumes of data
and create narrative content with minimal human intervention (Caswell & Dörr, 2017). is technological advance has
transformed the entire production chain, aecting the search for, production and distribution of journalistic content
(Diakopoulos, 2019; Rojas-Torrijos, 2021).
Although the study of the use of bots as a news service and for emotional connectivity with audiences is not new (Sánchez-
Gonzales & Sánchez-González, 2017), recent studies on the treatment of chatbots in the Spanish press highlight how AI,
and specically tools such as ChatGPT, have been received with a mainly informative approach, with greater emphasis on
their potential applications than on the risks, and dealing with it in an eminently informative and rarely interpretive manner
(Calderón & del Cid, 2024). is initial approach leads us to consider the many ways in which AI is having an impact on
journalism. Another recent study by JournalismAI says that 28% of journalists already use AI tools, and 20% plan to explore
the applications in the near future (Beckett & Yaseen, 2023). ere can be no doubt that these technologies are revolutionising
the drawing up of content, the personalisation of news, and workow optimisation, allowing journalists to generate content
with greater eciency and accuracy, as well as enhancing data collection and analysis (Carson, 2019; Vos et al., 2023; Shi
& Lin, 2024). Writing algorithms and news bots free professionals to dedicate more time to the interpretation and in-depth
analysis of information (Ufarte-Ruiz & Manfredi-Sánchez, 2019), improving content personalisation, something valued
positively by 70% of editors (Newman et al., 2023).
e speed with which we are witnessing these changes and their consequences in many professions has sparked the
interest not only of companies and professionals but also of academia. Academic research has addressed the study of AI
and journalism from multiple perspectives: data journalism, big data and bots (Flores-Vivar, 2019); journalistic innovation
and new formats ( d’Haenens et al., 2022); computational journalism and news output (Vállez & Codina, 2018); AI applied
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to news documentation and sports journalism writing (Segarra-Saavedra et al., 2019); the quality and truthfulness of both
information and misinformation (Graves, 2018; Canavilhas, 2022); its possible eect on job losses (Cid, 2017; Carson, 2019)
or ethical challenges (Ufarte-Ruiz et al., 2021), among others. It has also been studied from dierent theories of journalism.
Computational journalism theory has looked into how digital tools and algorithms are transforming traditional practices,
not only by automating repetitive tasks, but also by introducing new ways of gathering, analysing and presenting information
(Anderson, 2013). Moreover, mediatisation theory examines how technology is reconguring the relationship between the
media, journalists and audiences, aecting the way information is consumed and perceived, as well as the dynamics of
power and trust in the media (Hjarvard, 2013).
But the integration of AI into journalism is not free from challenges to ethics and transparency. Some academic research
has focused on analysing the possible job losses that it could bring about (Lokot & Diakopoulos, 2016; Valdiviezo-Abad
& Bonini, 2019), as well as the major impact on the quality of journalism. For example, although AI-generated news is
eective in presenting facts, it lacks the depth and context provided by human journalists, which aects the understanding
of complex issues (Carson, 2019). e ways in which AI can help combat disinformation through automatic fact-checking
systems has also been studied, but it also presents risks of spreading fake news (Graves, 2018). Research such as that of
Martín-García et al. (2024) points out that the key lies in eective human supervision to ensure that these tools are used
appropriately and ethically.
It should be noted that, although AI and journalism have been analysed from multiple perspectives, it is also necessary to
consider how journalists themselves perceive such integration. e opinion and experience of professional journalists are
of the utmost value, since they are the ones who directly face the challenges and opportunities that AI poses in their daily
tasks. It is in this aspect where the value and contribution of this research lies. By focusing on journalists’ perceptions and
experiences this study provides a practical and contextualised view of AI in journalism, exploring not only the technological
and ethical implications, but also the human and professional impact on the practice of journalism.
2. How AI is revolutionising journalists’ work: tools
Articial intelligence is not only redening the gathering, analysis and presentation of journalistic information, but is
also transforming the work dynamics of professionals in the eld. From photojournalists to newsreaders or editors, all
specialisations within the profession are inuenced by these technological advances.
One of the most signicant changes is the option of using AI tools to automate repetitive tasks. Well-known tools such as
Wordsmith and Heliograf allow journalists to focus on critical analysis and research, rather than on more mechanical tasks
such as proofreading, although this may raise concerns about the quality of information and job cuts (Carson, 2019). AI also
improves the eciency of information gathering through advanced natural-language processing algorithms, such as Google
News Initiative and Dataminr, which analyse large volumes of data and swiftly detect relevant patterns (Graves, 2018).
e rapid and ecient creation of multimedia content has been one of the tasks that has evolved the most. Tools such as
Lumen5 and Wibbitz allow the quick creation of high-quality audiovisual material. Journalists with little or no experience
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in video editing can turn out attractive, professional material. However, it is vital to maintain a balance between automation
and human supervision to ensure the quality and accuracy of the content.
Content personalisation is another aspect that has been deeply aected. News organisations can utilise AI to generate
articles and features tailored to the specic interests of their audiences, increasing reader engagement and brand loyalty.
However, this may create news bubbles that limit exposure to other perspectives, reinforcing pre-existing biases and further
polarising audiences (Newman et al., 2023). It is therefore crucial for news organisations to implement strategies to balance
personalisation with exposure to a variety of viewpoints (Pariser, 2022).
As regards fake news, AI oers automatic fact-checking systems. Tools such as ClaimBuster and Full Fact AI combat
disinformation by improving the precision of fact-checking (accurate verication of facts before publishing) and reducing
the spread of fake news. ese systems scan large volumes of data and detect content that needs to be checked, although
they require close collaboration between technologists and journalists to ensure that the algorithms are transparent and
take cultural and social contexts into account (Graves, 2018; Salaverría, 2021).
Analysis of sentiment and opinion is another area where AI is making a signicant mark. Sentiment analysis algorithms, such
as those employed by Brandwatch, assess tone and emotion in texts, providing journalists with a deeper understanding of
how their articles may be perceived. is technology is of particular use for analysing reactions on social media and adjusting
content strategies accordingly, although they can misinterpret context or sarcasm, leading to erroneous conclusions (Liu,
2021).
AI has made a noticeable mark on all areas of journalism, as there are tools applicable to dierent specialities. Television
journalists and newsreaders may nd that AI has facilitated the automatic transcription and translation of content.
Technologies such as Google’s Speech-to-Text and DeepL have revolutionised how interviews and reports are handled in
numerous languages, allowing news to be more accessible and have greater and reach. Such tools not only save time, but
also increase accuracy when transcribing dialogues and speeches, although they present challenges concerning accuracy
and cultural nuance that require human review and editing (Mayoral-Sánchez et al., 2023). Tools for photojournalism
such as Adobe Photoshop and Google Cloud Vision oer greater image quality and eciency, permitting the removal of
unwanted objects, automatic adjustment of light and colour levels, or more ecient cataloguing of images. A debate has
arisen here about the authenticity of AI modications, questioning where the line lies between digital enhancement and
manipulation (omson et al., 2024).
e future of AI in journalism promises even greater innovations. Augmented reality (AR) and virtual reality (VR), upgraded
by AI, are beginning to be used to create immersive experiences in news reporting. ese technologies allow viewers to
explore events and places interactively, providing a richer and deeper understanding of stories (Pavlik & Bridges, 2013;
Baía et al., 2023). Furthermore, the ongoing development of language models such as GPT-4 and others is opening up new
possibilities for creative and collaborative content generation between humans and machines. ese models are capable
of writing complex articles, conducting simulated interviews, and generating detailed analysis, thus expanding the options
open to journalists (Brown et al., 2020).
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e incorporation of these emerging technologies also presents challenges, such as the need for advanced technical skills
and a deep understanding of the ethical implications. However, with proper management, these innovations can provide
powerful tools for telling stories in novel ways, enhancing narration and the audience’s understanding (Pavlik & Bridges,
2013).
Table 1 shows a list of the main AI tools applied to dierent areas of journalism with their advantages, limitations and
functionalities.
Table 1. AI tools applied to journalism
Task / FunctionalityAI tool
AdvantagesLimitations
Automation of Repetitive
Tasks
Wordsmith, Heliograf
Time freed up for complex
tasks.
Possible job losses.
Optimising Information
Gathering Processes
Google News Initiative,
Dataminr
More ecient
recompilation.
Risk of errors in data
analysis.
Photojournalism and
Image Recognition
Adobe Photoshop, Google
Cloud Vision
Greater image quality and
eciency.
Debate on the authenticity
of the images.
Personalisation of Content
Algorithms from news
organisations
Increase reader
engagement.
Possible creation of news
bubbles.
Fact-checking and
Combating Disinformation
ClaimBuster, Full Fact
Greater speed and
accuracy of fact-checking.
Requires close
collaboration between
technicians and journalists.
Automatic Transcription
and Translation
Speech-to-Text by Google,
DeepL
Save time and increase
accuracy.
Challenges related to
accuracy and cultural
nuances.
Analysis of Sentiment and
Opinion
Brandwatch, Crimson
Hexagon
Report on the emotional
impact of articles.
Limitations to the
interpretation of context.
Generation of Multimedia
Content
Descript, Lumen5
Facilitates the creation
of high-quality visual
materials.
Requires human
supervision to ensure
quality.
Ethics and Human
Supervision
Human-AI collaboration
systems
Ensures integrity and trust.
Implications of algorithmic
bias.
Future Innovations and
Emerging Trends
AR and VR tools, GPT-4
Provides powerful tools for
storytelling.
Requires advanced
technical skills.
Source: created by the author
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As the table shows, these AI tools cover a wide range of functionalities in journalism. Wordsmith and Heliograf, for example,
enable automated news generation, freeing up time for journalists to focus on more complex tasks, while ClaimBuster and
Full Fact AI improve the accuracy and speed of fact-checking. Tools such as Adobe Photoshop and Google Cloud Vision
facilitate ecient image editing, although they open up debates about visual authenticity. Technologies such as Google
DeepL reduce work time in transcription and translation, and sentiment analysis systems, such as Brandwatch, provide
a deeper understanding of public reactions. Despite their usefulness, journalists can (and should) review the results such
tools create to identify possible errors and ensure that both the data utilised, and the information provided are accurate.
3. Objectives and Methodology
3.1. Objectives
e general objective of this study is to explore, from the perspective of professional journalists, the impact of AI on the
many tasks that make up their daily work. To this end, three research questions are posed to analyse the opportunities,
challenges and implications associated with its implementation:
1. What opportunities does AI integration oer to improve eciency and creativity in journalism?
2. What are the main challenges and concerns facing journalists with the use of AI?
3. What is the impact of the introduction of AI on the job market for journalists and what skills should journalists develop
to adapt to these changes?
3.2. Methodology
A double methodology was chosen to better answer these questions: namely, bibliographic review and then qualitative
analysis through semi-structured interviews with Spanish journalists. First, an exhaustive bibliographic review on AI and its
impact on journalism was carried out, based on highly prestigious sources, including papers published in journals indexed
by Journal Citation Reports (JCR) and Scimago Journal & Country Rank (SJR). is review addressed key topics such as
the automation of news, content personalisation, fact-checking, and data journalism. Bibliographic review is essential
in academic research, allowing the compilation, evaluation and synthesis of relevant information (Martín & Lafuente,
2017). is process eases the identication of emerging trends and persistent challenges, providing a solid basis for the
study’s analysis and conclusions (Kitchenham & Charters, 2007). Secondly, a qualitative analysis was carried out based
on semi-structured interviews with journalists. is methodology is suited to the in-depth consideration of complex and
contextualised phenomena (Denzin & Lincoln, 2018), ensuring a rich, nuanced understanding of participants’ experiences
by going more deeply into their subjective impressions (Kvale, 2007). Interviews for this research were structured into four
blocks: questions concerning socio-demographic prole (profession, age, years of experience and specialty), daily use of AI
at work (tools they use, and their impact), challenges and opportunities they present in the profession (perceived advantages
and limitations) and, nally, their vision of the future (repercussions for the labour market, and skills to be developed).
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Selection of the sample was based on a non-probabilistic procedure, combining convenience and snowball sampling,
prioritising diversity over representativeness since it is more eective in qualitative studies (Dyas, 2009; Papathanassis &
Knolle, 2011). Two lter conditions were set for the selection of participants: having a degree in journalism and being active
or having been so in the last 5 years. 12 personal interviews were conducted online through Google Meet.
e interviews were
conducted in March 2024 and lasted approximately one hour. is number of interviews achieved data saturation, a widely
accepted methodological principle in qualitative research (Saunders et al., 2018). Data saturation indicates that further
data gathering is unnecessary once sucient data has been collected (Hennink & Kaiser, 2021). After that point additional
interviews no longer reveal new aspects or issues. Data saturation could be considered the equivalent in qualitative research
of representativeness in quantitative studies (Papathanassis & Knolle, 2011). e proles of the interviewees are set out in
Table 2.
Table 2. Personal interview prole sheet
IntervieweeAgeProle
Years of
Experience
Length of
Interview
Medium
Geographical
scope
139
Sports journalist
5
45 minutes
RadioNational
241
Freelance
journalist
10
50 minutes
TVRegional
328Photojournalist3
50 minutes
TVInternational
456
Radio Host
20
60 minutes
RadioLocal
545Columnist13
55 minutesWritten press
National
626
News Editor
1
60 minutes
RadioRegional
732
Data Journalist
3
55 minutes
Economic news
agency
National
849
Agency
Correspondent
12
News agency
International
954
Television
Investigative
Journalist
24
60 minutes
TVNational
1061
Economic
Journalist
30
45 minutes
Digital
newspaper
National
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1133
Agency
Journalist
6
55 minutesNews agency
National
1229
News Editor
2
40 minutes
Digital
newspaper
Local
Source: created by the author
ematic analysis was utilised to analyse the interviews. Its chief objective is to identify, analyse and report patterns
(themes) within qualitative data, oering a detailed and nuanced interpretation of the content (Braun & Clarke, 2006). is
method focuses on the experiences and perspectives of individuals and groups, allowing for a deep understanding of how
they perceive and make sense of their surroundings (King, 2004). e analysis procedure was carried out in three phases:
familiarisation and transcription of the interviews, generation of initial codes and grouping of codes by themes. Table 3
shows the process followed.
Table 3. Phases of qualitative analysis
Phases of qualitative analysis
Phase 1
Familiarisation and transcription
Phase 2
Initial code generation
Phase 3
Code grouping and renement of
themes
Source: created by the author
In the primary phase of familiarisation and transcription, a detailed transcription of the interviews was made, including
notes on possible emerging themes and relevant contextual details (Riessman, 1993; Poland, 1995). In the second phase,
initial codes were generated using open coding techniques, organising the data with NVivo software (Strauss & Corbin, 1998;
Jackson & Bazeley, 2013). emes were sought in the third phase, and rened by grouping codes into broader themes, using
the constant comparison method to assess the coherence and relevance of the themes in the context of the study (Glaser
& Strauss, 1967). emes identied included: eects on the production of content, ethics and transparency, the impact on
employment, the quality and veracity of information, the relationship with the public, and innovation and the future of
journalism, with specic codes assigned to each theme, as shown in Table 4. is approach allowed for a rich and complex
understanding of participants’ narratives, providing a solid basis for the interpretation of qualitative data (Patton, 2002).
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Table 4. Rened codes and themes
emesCodes
Eects on content production
Automation of editing, news generation, content
personalisation
Ethics & transparency
Algorithmic bias, transparency in the use of AI, ethical
responsibility
Impact on employment
Mobility of journalists, new job opportunities,
professional adaptation and training
Quality and truthfulness of information
Automated fact-checking, precision in news generation
and disinformation
Relationship with the public
Interaction with readers, trust in AI-generated content,
relationship with the audience
Innovation and the future of journalism
New forms of narrative, data analysis tools, future AI
applied to journalism
Source: created by the author
4. Results
e results of the interviews reveal a mix of optimism and concern about the adoption of AI in newsrooms which respond
to the research questions posed.
4.1. Opportunities for AI to improve eciency and creativity in journalism
Analysis of the journalists’ discourse reveals a generally positive perception of articial intelligence (AI) as a transformative
tool in journalism. Benets such as automation of repetitive tasks, improved fact-checking and personalised content are
highlighted. ese capabilities not only increase eciency and enrich research quality, but also serve to expand the global
reach of journalism through the automatic translation of articles, underlining the innovative potential of AI in the media
industry.
One of the most noted benets is AI’s ability to automate repetitive, low-value tasks, freeing journalists to focus on more
creative and analytical work: “
AI streamlines the data collection process. Otherwise, we’d nd it more dicult to work with
large volumes of data and information, it simplies the more routine daily tasks
.” is aspect allows journalists to spend more
time on tasks that require creativity and analysis, thus improving the quality of their work. However, interviewees point out
that, although AI makes data processing faster, it is still human judgment that adds real value to news:
“Sure, AI allows us
to process a lot of data and information in seconds and makes certain more repetitive tasks easier, but it is our judgment that
turns the data into interesting, well-told, truthful stories.”
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Value is appreciated in AI’s ability to identify patterns and trends that would normally go unnoticed, it signicantly improves
the quality of a journalist’s research. Eciency in analysing large amounts of data not only saves time, but also enriches
the quality of research: “
We can review huge databases. at helps us discover stories that we would otherwise not be able
to detect. But there’s no replacement for our work in selecting, writing or sifting, at least not for now
.” is ability to review
huge amounts of data and nd relevant information seems especially useful in the case of investigative journalism, when
complemented by human endeavour.
ere is also a signicant improvement in fact-checking, a crucial function in a media environment where speed and
accuracy are essential to maintaining credibility: “
AI makes us more ecient at fact-checking and I think we can also identify
false news more quickly and accurately, but our job as a human is essential – an intelligent machine or a program can’t do it in
the same way
.” Nonetheless, even though AI has revolutionised fact-checking, many journalists emphasise that their role is
still essential in giving context and depth to information:
“AI helps us detect false news with impressive speed, especially when
we are working against the clock. But in the end, we’re the ones who make sense of the facts and understand their import.”
AI oers benets by personalising content, transforming the way readers receive their news. e reading experience
becomes much richer and more meaningful, as each piece of content is carefully selected to capture the reader’s attention
and keep them informed about what matters most to them: “
Being able to open a news app and nd articles that perfectly
align with your hobbies, your work or your favourite topics of interest instead of being bombarded with generic information
is of great value to both media and readers
.” Despite the obvious benets that AI oers in personalising content, some
journalists stress the importance of balancing this capability with information diversity:
“e challenge is to nd a balance
between personalisation and information diversity. It is up to us, journalists, to ensure that this personalisation does not limit
access to a broader view of the world.”
In sports journalism, AI enables instant reporting, which is crucial for covering events in real time: “
When I’m covering a
football match or sporting event, AI gives me immediate access to the latest, most important data. I can then write and publish
detailed analysis almost instantly. is speed and eciency is crucial for delivering accurate and comprehensive live coverage
.”
Regarding the creation of multimedia content, there is unanimity that AI models can edit videos and improve audio quality,
freeing journalists to focus on the narrative and context of the story: “
AI helps us generate high-quality multimedia content
more eciently, but the journalist’s mind is still unmatched by any AI
.”
AI also opens up new possibilities for innovation in narration, facilitating experimentation with formats such as augmented
reality and virtual reality. ese tools oer innovative and engaging ways to tell stories, allowing readers to live immersive
experiences and better understand information: “
AI tools allow us to experiment with new narrative formats such as
augmented reality and virtual reality. at’s all well and good, but the nal word, the compositions, are the journalist’s job.
e human touch is still vital in our profession today
.”
Another positive aspect is AI’s ability to automatically translate content, expanding the global reach of journalism. is
is relevant to disseminating important news worldwide quickly and eciently: “
With AI we can automatically translate
articles into many languages, which helps us reach more people around the world. ere we have gained a lot in both time
and cost. But there are other things that call for a journalist’s brain. I don’t watch any TV or radio programmes presented by
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a machine. I doubt I’ll ever watch one
.” In this regard, we know that there are already presenters created with AI. is is the
case of Mediaset with its rst AI-created presenter hosting the programme ‘Supervivientes’, or Natcha, the ai television
news presenter also created using AI. Nevertheless, the journalists interviewed for this paper unanimously point out that,
although they are technically impressive, they fail to replicate the human factor, especially when it comes to the emotions
they can convey:
“AI can do loads of things, including present the news, but it can’t feel. I don’t think that viewers’re just
looking for data; ey’re looking for empathy, human connection, that’s what sets us apart, and at least for the moment, I don’t
believe technology can oer it. at’s what really sets us apart.”
4.2. Main challenges and concerns faced by journalists regarding the use of AI
e study also reveals signicant concerns about the use of AI in journalism. Challenges include the loss of essential
skills, the possible homogenisation of content, and the greater spread of fake news. Furthermore, the use of AI in content
moderation may result in inappropriate decisions and a disconnect from the audience. e need for human oversight and
appropriate regulation to maintain the integrity of journalism is apparent.
One of the biggest perceived risks is the potential erosion of quality and depth in investigative journalism, because an
over-reliance on these systems could lead to a loss of these values in investigative journalism:
“e lack of a human touch
and contextual understanding can ultimately make reporting much more supercial or biased. I think that in the end, no
matter how much AI is used, a journalist has better criteria.” “In photojournalism, AI seems like magic, but a photo is not
just an image, it’s heart. AI doesn’t have that. I believe and hope it never does”
Similarly, over-reliance on AI could lead to
a decline in the critical and analytical skills of journalists themselves:
“If we get used to AI doing all or a great part of the
work, we could lose our essential investigative and analytical skills
.” is could turn out a generation of journalists who are
less skilled at conducting in-depth research without the help of technology. Another risk is the possibility that AI could
amplify existing biases from the data it is trained with
: “AI learns from previous data, and if that data contains biases, AI
will replicate and amplify them
.” is can perpetuate stereotypes and prejudices, something particularly problematic in a
eld that should aim for objectivity and fairness. Finally, there is also the risk that AI will be used to conjure up fake news in
more sophisticated ways:
“Using AI, fake news can be created and distributed faster and more convincingly, making the ght
against disinformation even more dicult
.” e journalists stress that this risk could jeopardise public trust in the media and
in the veracity of the news they receive.
ere is also concern about the potential homogenisation of content. Because AI algorithms tend to rely on patterns and
prior data, one piece of content can become too similar to another, diminishing the richness of journalistic discourse:
“Automating news creation could lead to all stories being very similar, which would greatly reduce the variety of viewpoints
and opinions, an important part of our profession
.” is risk of uniformity in news threatens to diminish the diversity needed
for robust and meaningful journalism.
Furthermore, there is concern that the use of AI may moderate content on social media. AI’s automated decisions do not
always manage to understand the cultural or political context, which can lead to censorship of valuable opinions: “
We
cannot leave certain issues up to AI. ere’re choices that should be made by people, not by machines or algorithms
.” is lack
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of contextual understanding by AI can result in the unfair removal of relevant and valuable content, thereby diminishing the
diversity of journalistic discourse. AI, which bases its decisions on patterns and prior data, may fail to pick up on important
nuances and specic contexts, leading to decisions that do not adequately reect the complexities of real-life situations. is
underlines the need for human oversight in content moderation to ensure that the richness and diversity of discourse on
social media is maintained, preventing uniformity that would impoverish public debate: “
Human intervention is essential
to ensuring that the decisions taken are not only fair, but that they reect the variety and depth of our cultures and opinions
.”
As regards sources of information, the interviewees point out that trust between journalists and their sources is essential
to journalism. If sources feel that they are talking to a machine or that their words will be analysed out of context by an
algorithm, they may be less open and frank: “
I can’t imagine one of my sources having to give information, which may
be sensitive, to a machine
.” Such a lack of trust would be worrying because it could aect the quality and depth of the
information obtained. e relationship of trust between a journalist and his/her sources is fundamental to ensuring that
the information shared is both truthful and complete. ere is also a concern that AI could create a disconnect between
journalists and their audience. “
AI can make us lose the personal touch with our audience, making our news feel colder and
less human
.” is disconnect could damage the relationship of trust that is crucial for readers to feel connected and to trust
the news. AI’s ability to subtly manipulate information is also seen as a threat to the profession of journalism: “
e idea that
AI can be used to subtly manipulate information is a catastrophe for our profession because where does that leave us?
” is
concern underlines the need for proper supervision and regulation to prevent AI from being used unethically and eroding
public trust.
4.3. Impact of the adoption of AI on the labour market for journalists and the skills that journalists need to adapt
to those changes
e results reveal that the adoption of AI in journalism raises concerns about unemployment and the transformation of the
labour market, chiey aecting repetitive tasks, while roles with human presence seem less threatened. e interviewees
highlight the ethical risks related to privacy and the chance of errors and biases without adequate supervision. In addition,
there is uneasiness that AI may increase workloads by requiring greater supervision and correction. Despite these concerns
there is emphasis on the need for a harmonious, ethical integration of AI, developing complementary skills that highlight
the unique human aspect which cannot be automated.
Concern and uncertainty about unemployment and the transformation of the labour market that AI may bring about is
a common denominator in the interviewees’ discourse:
“AI scares us because we think that our profession, to some extent,
may disappear… this question is widespread across many other professions”; “I think that this should start to be considered
in the universities. AI is here, it’s a reality, we’ll no longer be able to work without it. Faculties should be teaching how to use
these tools to the benet of journalists, to make them even better professionals.”
is perception does not seem to aect all
journalists’ tasks equally; repetitive tasks, such as data-driven writing, are seen as more easily automatable, while roles
requiring human presence, such as newsreaders, are apparently seen as being under less of a threat: “
AI doesn’t aect a
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news anchor in the same way as it does an editor or photojournalist… a newsreader… can’t be replaced so easily because they
provide the human touch. It wouldn’t make sense
.”
Regarding risks to privacy and ethics, the interviewees suggest that the increasing use of AI raises serious ethical questions
concerning privacy and data use. Monitoring and analysing user behaviour to personalise content can become invasive,
and without proper regulation risks eroding public trust. is concern is increased by the potential for AI to introduce errors
and biases:
“One of my biggest concerns is that AI can introduce errors and biases without proper oversight
; “
I’m worried
about ethical issues, fake news, identity theft…”
ese concerns reect the ethical challenges that must be faced to ensure the
integrity of journalism.
ere is also concern that AI may increase the pressure and workload on journalists rather than reducing them: “
Although
AI can automate many tasks, it may also mean that we have to check and correct more, adding to our workload, because we
can’t assume that everything AI churns out is right
.” is increased workload not only aects journalists’ eciency and well-
being at work, but may also have a negative eect on the quality of the content generated. By having to spend more time on
monitoring and correction, journalists could actually have less time to carry out in-depth research and build relationships of
trust with their sources. ere is a widely-held opinion that there is a need to nd the right balance in the use of AI to ensure
that these fundamental aspects of journalism are not compromised: “
If we are busy correcting AI errors, we won’t have time
to do quality journalism. We need to use the technology in a balanced way so as not to compromise our quality of work
.”
Despite these concerns, the journalists agree that there is a need to integrate AI harmoniously and ethically into the practice
of journalism: “
AI is here to stay, and we have to live with it. We can’t see it as an enemy. We need to learn how to use it
as a complement to our work
.”
“We should look on AI as a tool that lets us give the best version of ourselves as journalists,
not as a substitute
.” is perspective underlines the importance of a balanced, deliberate adoption of the technology. e
interviewees unanimously agree that professional journalists need to develop skills to complement AI, focusing on tasks
that highlight their value: “
e human touch is what really connects with people and keeps their trust. Although AI advances,
we need to focus on what makes us unique and can’t be replaced by machines
.”
5. Discussion and conclusions
e aim of this study was to look more closely, from the perspective of professional journalists, at AI’s impact on their
work. e ndings conrm that AI has emerged as a transformative tool in journalism, oering signicant benets but also
presenting ethical and professional challenges. Mastering AI is essential for journalists to improve their employability in
an increasingly technological labour market. e advantages oered include the potential these tools have to automate
repetitive and low added value tasks, freeing journalists to focus on more analytical and creative tasks where human input
is irreplaceable (Hepp & Loosen, 2021; Stenbom et al., 2023). Apart from saving time and simplifying complex processes,
the quality of journalistic research is improved (Brennan et al., 2020). New possibilities present themselves to innovate in
aspects such as narratives or mining large databases to discover previously hidden stories, something of particular value
to investigative journalists. AI’s usefulness is also apparent for experimentation with new formats such as augmented and
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virtual reality, oering immersive experiences and adding to the quality of journalists’ research (Beckett, 2019; Mandela,
2024). e integration of AI in journalism adds to journalists’ ability to oer a more precise, ecient and enriching version
of their work, something of benet to both the industry and society (Túñez-López & Tejedor-Calvo, 2019).
However, major challenges have also arisen. Over-reliance on automated systems may lead to the production of supercial,
impersonal and biased content, compromising the integrity and diversity of journalistic discourse (Diakopoulos, 2019).
Journalists’ integrity and ethics, together with their ability to provide context and critical analysis, are indispensable for
the generation of truthful, meaningful news (Susskind & Susskind, 2020). Concerns about the eects of AI on employment
and changes to the labour market cannot be merely brushed aside (Cid, 2017); a perception that varies according to one’s
specialisation and specic eld within the profession. Structured, repetitive tasks, such as writing data-driven news, are
more susceptible to automation and there is therefore a greater fear of job substitution. As is the case with photojournalists,
who face direct competition from technologies that can generate images, and therefore feel more threatened (Hausken,
2024). Roles that require signicant human presence and greater interpersonal skills, such as news anchors, seem to feel less
vulnerable. e ndings underline the crucial need for professional journalists to develop skills that can be complementary
to AI, focusing on tasks that are not so easily automated. Wölker & Powell (2021) underlined the importance of journalists
focusing on those tasks that, by their very nature, cannot be easily automated. AI facilitates the compilation and analysis of
enormous amounts of data, but human abilities to detect nuances, understand sociocultural contexts and exercise critical
thinking are essential to maintaining the quality and rigor of journalism. We are speaking here of skills that have to do with
the ability to understand and interpret surroundings, and the ability to keep watch over the powerful, among many other
functions that call for creativity and real, not simulated, intelligence (Codina et al., 2022).
According to the journalists themselves, the integration of AI into their work must be approached proactively, critically and
sensibly. Keeping a balance between automation and human participation is crucial to ensuring the quality and integrity
of content. Hence the importance of ethics, transparency and critical thinking in both its use and training (Lopezosa et al.,
2023). e ndings indicate that AI tools not only bring new possibilities to newsrooms, but that their eectiveness depends
on how they are used and who is using them. is underlines how AI changes the way journalists work and, at the same time,
how journalistic work inuences the development and application of AI (Carlson, 2015; Jarrahi et al., 2023).
Academia has the responsibility to lead this transition, with the incorporation of AI training into educational programs
(Flogie & Aberšek, 2021). Communication faculties cannot just stand idly by and watch changes of such importance to
society, changes that are shown to have a wide impact on employability (Lopezosa et al., 2023). It is vital that future journalists
acquire technical skills and overcome any fear of these technologies, seeing their potential as complementary tools rather
than threats (Calvo-Rubio & Ufarte-Ruiz, 2020). Education must therefore focus on the ethical and competent use of AI,
preparing students to eectively and responsibly integrate these tools into their professional practices. Professionals in
the world of communication need to know, handle and understand very clearly what AI systems are and how they work.
It is therefore time to talk about professional training that needs to include AI (Codina, 2024). Media outlets also have a
crucial role to play in this transformation. Providing ongoing training, access to advanced AI tools, fostering an environment
that values technological innovation, and empowering sta would not only serve to improve the quality of journalism, but
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could also ensure the competitiveness and relevance of media outlets. Nevertheless, despite these ndings, this research
has certain limitations. Chief amongst these is its qualitative approach based on personal interviews, which may restrict
the generality of the results. Although a deep and detailed insight into journalists’ experiences and perceptions has been
achieved, the ndings may not be fully representative of the entire sector.
Future lines of research should consider the development of specic methodologies for teaching AI in journalism courses,
as well as in empirical studies that evaluate the eectiveness of these methodologies. ere is also a need to investigate
how media companies can better structure their investments in AI technologies and ongoing training in order to maximise
benets. Another crucial aspect is to continue to delve deeper into the ethical and social eects of using AI in journalism,
ensuring that these tools are used in ways that benet society as a whole.
Only through research, ongoing education and technological adaptation will it be possible to ensure a future of ethical,
innovative journalism capable of meeting the demands of a society in constant technological evolution. AI does not replace
journalism as a profession; it should be understood as an ally that oers signicant opportunities to improve and perfect
work. Journalists bring editorial judgment, cultural and ethical sensitivity, and the ability to establish relationships of trust
with sources, attributes that machines cannot yet replicate. It is essential to lose the fear of these tools and understand their
potential to raise the quality of journalists’ work.
6. Acknowledgements
is research was made possible thanks to the generous collaboration of the journalists who participated in the study,
whom the author wishes to sincerely thank for their valuable contribution.
is article has been translated into English by Brian O´Halloran, to whom we are grateful for his work.
7. Conict of interest
e author declares that there is no conict of interest contained in this article.
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Articial Intelligence and Journalism: exploring journalists’ perspectives
ISSN: 1696-019X / e-ISSN: 2386-3978
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