Robot Journalism Revolution and Adoption: Dynamics and Dilemmas Related to Artificial Intelligence in the Press Field

Abstract

The information variability caused by the cybernetic nature of the Internet is turning governments across the world including African countries against social and business online platforms; however, new media and AI participation has since become a dynamic trend of newsroom activities. Through Artificial Intelligence (AI), tech giants, such as OpenAI, Tencent and Automated Insights are creating support tools for journalists to improve their news gathering and production capacities with the exordium of robot journalism. However, the ethical dilemma of automated journalism, especially authorship, who should be liable or answerable for the information generated and conveyed by the AI including loss of work visa -vis the fate of human journalists is now against robot journalism. As such robot journalism is yet to be generally incorporated and for good cause too. Hinged on the theory of Diffusion of Innovation and the Utilitarianism theory, this study explored ways automated journalism is utilised, adopted and checkmated. Data was sourced from existing literature, which included books, journals, news reports and online materials. Findings show that robot journalism has a relative advantage as it outperforms newsroom conventions in terms of speed, ultraprecision and fairness. Recommendations require media industrialists to advance their technical capacities enough to respond to these advancements and maximise them for efficiency.

Full text

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
10
AUTOMATED JOURNALISM REVOLUTION AND ADOPTION: DYNAMICS AND DILEMMAS
RELATED TO ARTIFICIAL INTELLIGENCE IN THE PRESS FIELD
By
Roxie Ojoma Ola-Akuma, PhD
Department of Mass Communication
Bingham University, Karu, Nasarawa State, Nigeria
Email: roxie.ojay@gmail.com
Desmond Onyemechi Okocha, PhD
Department of Mass Communication
Bingham University, Karu, Nasarawa State, Nigeria
Email: desmonddoo@yahoo.com
Melchizedec J. Onobe, PhD
Department of Mass Communication
Bingham University, Karu, Nasarawa State, Nigeria
Email: melchizedek.o@binghamuni.edu.ng
Richard Okujeni, PhD
Department of Mass Communication
Bingham University, Karu, Nasarawa State, Nigeria
Email: okusrich@yahoo.com
Abstract
The information variability caused by the cybernetic nature of the Internet is turning governments across the world
including African countries against social and business online platforms; however, new media and AI participation
has since become a dynamic trend of newsroom activities. Through Artificial Intelligence (AI), tech giants, such as
OpenAI, Tencent and Automated Insights are creating support tools for journalists to improve their news gathering
and production capacities with the exordium of robot journalism. However, the ethical dilemma of automated
journalism, especially authorship, who should be liable or answerable for the information generated and conveyed
by the AI including loss of work vis-a-vis the fate of human journalists is now against robot journalism. As such
robot journalism is yet to be generally incorporated and for good cause too. Hinged on the theory of Diffusion of
Innovation and the Utilitarianism theory, this study explored ways automated journalism is utilised, adopted and
checkmated. Data was sourced from existing literature, which included books, journals, news reports and online
materials. Findings show that robot journalism has a relative advantage as it outperforms newsroom conventions in
terms of speed, ultraprecision and fairness. Recommendations require media industrialists to advance their technical
capacities enough to respond to these advancements and maximise them for efficiency.
Keywords: Robot Journalism, Technology, Artificial Intelligence, Utilitarianism, Internet
Introduction
To appreciate Artificial Intelligence (AI), one needs to consider some events from seemingly mundane home tasks
to official and academic activities. For example, we can consider how many man-hours would have been required by
peer reviewers in 1990 to check an article for plagiarism. This would include the volume of materials that would
have to be accessed and the number of people that would have to perform this herculean and rigorous task. The
emergence of automated word checkers, now only requires a peer reviewer to simply upload the whole or part of a
text on provided platforms such as Quillbot or Voila and the assessment is done in seconds. The upsurge of Corona
Virus, first detected at the end of 2019 was a wake-up call for media practitioners across the world to leverage on
existing technologies that can keep them safe while they work. With the experience of the first wave, it was quite
uncertain how key participants would cope with the burden of how to control the spread of what has become the
COVID-19 pandemic, should it rise again (OECD, 2020). Although it is tough to distinguish between media
practitioners who got sick at work or in their personal affairs, the Swiss organisation, Press Emblem Campaign

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
11
(PEC) in 2021 reported that the pandemic had claimed the lives of over 600 journalists around the world (Press
Emblem Campaign, 2021). As at March 2022, at least 1,994 journalists had succumbed to the virus across 95
different countries. Among the most affected nations were Brazil, India, Peru, Mexico, and Colombia, with Brazil
reporting the highest number of deaths at 314, followed closely by India at 284, and Peru at 199. Other countries
with notable fatalities included the United States, Bangladesh, Italy, Venezuela, and Ecuador (Press Emblem, 2024).
Not only were journalists affected by the pandemic, they were caught in the net of wars and crises zones as reported
by the Committee to Protect Journalists (CPJ) and the Press Emblem Campaign (2021). Also, as new media
platforms such as Twitter, Facebook, Instagram are becoming sites with user-generated contents (UGC) which news
organisations increasingly rely on for news, one cannot begin to imagine how many additional staff would be
needed to manually monitor, filter and verify the UGCs before curating them for news. Without technology, this
would be a time and effort consuming task (Heravi et al., 2014).
In some climes, natural disasters such as earthquakes, floods, have posed a risk to journalists and during these times
one could only wish there was another way to still source fresh news. In 2020, Patrick White in a news report said:
“The journalism world needs to keep pace with the evolution of new technologies; newsrooms need to leverage on
what artificial intelligence can offer.” That statement came after a headline that read: Covid Has Been Hard On
Journalism. AI Can Help Without Taking Your Job (White, 2020). This explains why “at the height of the
pandemic, some newspapers were being produced without anyone in the office – an industry first.” (Newman,
2021). This calls for journalists to continue to stay safe while they still find new ways to provide accurate
information, and for news agencies to survive economically and socially.
The examples above do not reflect news, as ‘accurate’ information has become a rare commodity with the
proliferation of plagiarised contents and faux news, which can be relayed in the ‘tweet of a second’. In places where
there is no structurally-sound infrastructure, reporters have to be sent to the field to cover news because the news
must go on. This has become unusually difficult due to the high risks associated with going out.
Professionals in the field of journalism are increasingly influenced by the ability of netizens to publish their own
content on various online platforms due to the development of digital technology, which has transformed traditional
newsroom norms and practices (Towne, 2020). The evolution of journalism from the inception of photography in
1685, led to the emergence of photojournalism newspaper styles between 1925 to 1975 and the significant transition
to moving pictures for broadcast on television and movies (Akinfemisoye, 2014). Additionally, the development of
computer software tools such as intelligent multimedia communication has led to advanced forms of multimedia
content creation (Seng et al., 2022). These sets of software according to Monique (2023) allow users to create and
edit videos, audio and images in a more efficient and effective manner. Furthermore, smartphones with built-in
audio recording features and apps that can convert audio to text have democratised news reporting, leading to a shift
towards mobile journalism or remote reporting (Granger, 2019). The rapid dissemination of news through social
media platforms has also had a profound impact on journalism, making it more engaging and fascinating (Leigh,
2017).
It is against this backdrop that this study sought to explore the adoption of robot journalism and the arising ethical
considerations associated in the light of new developments.
Research Questions
This study attempted to provide answers to the following:
1: How has robot journalism been adopted in newsrooms across the world?
2: What are the global constraints in adopting robot journalism?
Theoretical Framework
This research is predicated on two theories: the diffusion of innovations theory been in the main theory and
supported by the normative ethical theory of Utilitarianism. This is because the researchers believe that these
theories focus on the subject matter which is adoption and ethics.

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
12
Diffusion of innovations theory reveals the ‘means by which’ (how), motive (why), and to what degree an
innovation and technology spreads. Propounded by Everett Rogers in 1962, and further modified in 2003, the theory
posits that diffusion is the process by which an innovation is conveyed over time to a people in a community and
that any innovation must be utilised and adopted in order to spread. As a result, Rogers proposes that four main
elements must be present for that diffusion to occur - the innovation, medium of communication, time and a
community. Like any innovation, automated journalism relies heavily on the people, and for it to become
sustainable, it must be widely adopted by the people; in this case media agencies. Rogers goes on to illustrate that at
a certain point in the rate of adoption, an innovation could reach critical mass, which in this case means that enough
media and news organisations must have adopted automated journalism for the rate of adoption to become self-
sustaining, similar to the smart phones and the internet explosion (Gruenbaum, 2015). Rogers categorises adopters
into 5 groups which are: the ground breakers (innovators), pioneers (early adopters), early majority, late majority,
and laggards (Lopez, 2015).
While the innovators want to be the first to try the automated journalism, the late majority are skeptical of the
process. The last are the laggards, those satisfied with the traditional way of doing things. One of such examples is
that despite the Covid-19 pandemic in Nigeria, quite a number of institutions have refused to use and adopt available
online meeting platforms to do their tasks (Olasunkanmi, 2020). In line with the diffusion of innovation theory,
these are the laggards, accustomed to the norms and are comfortable therein. They are the most resistant to change
and the most difficult to persuade. They question what AI does and want to know in clear terms, the limits to the
freedom of information when it comes to engaging AI technology.
Like many theories, some criticisms have been aired on the diffusion of innovation theory. Some experts say
measuring diffusion is difficult to quantify because humans and social networks are complex. It is extremely
difficult, if not impossible, to measure what exactly causes adoption of an innovation (Damanpour, 1996). This is an
important factor, in the media. Those who advocate for adoption of automated journalism or improved media
technologies must be aware of the many forces acting on a practitioner’s decision to learn a new skill or capitalise
on a technology. Rogers (2003) responds with the five (5) attributes that influence adoption rate – relative
advantage, compatibility, complexity, trialability, and observability. These are the perceived attributes of an
innovation and in this case, robot journalism that will steer the discussion on issues raised for the technology's
adoption.
Another criticism is that diffusion theories can never account for all variables, thus, may overlook the primary
beneficiaries of adoption according to Plsek and Greenhalgh (2001), leading to inconsistent results in research that
further reduces the empirical value (Downs, 1976). However, the theory is still relevant to this study and for
discussing spread and technology adoption.
Utilitarianism Ethics Theory
Utilitarianism is a normative ethical theory propounded by Jeremy Bentham and John Stuart Mill. The theory places
the status of right and wrong exclusively on the results of choosing one action/policy/technology over another
(Tardi, 2023). Bentham and Mill laid the groundwork for this theory during the late 18th and 19th centuries in
England. The situation/circumstance that led to the propounding of the theory, was amid the societal changes and
intellectual ferment of their time, they sought to address fundamental questions about morality and human behaviour
(West & Duignan, 2024). This was because they argued that humans grappled with how to determine what is right
or wrong in a consistent and rational manner.
Thus, their response was utilitarianism, which aimed to provide a systematic approach to evaluating actions based
on their consequences. As a result, it considers the interests of others in addition to one's own. In the case of robot
journalism, adopting an AI would be said to be for the interest of the journalists to make work easier. Furthermore,
there are three principles postulated for utilitarianism: Pleasure or Happiness as central values; the outcome of an
action establishes right or wrong (Evans, 2023). One can look at the case of the Microsoft Network News, which
used AI and afterwards had to fire twenty-seven employees who were no longer needed, the outcome here is not a
happy one for the staff laid-off but a happy one for the company that has reduced cost. The third principle says
happiness counts equally for everyone (Westacott, 2019); this means it should be a win-win situation for all.

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
13
Amidst the postulations of this theory, critics however argue that utilitarianism can sometimes overlook individual
rights and justice. Sacrificing one person’s well-being for the greater good may raise ethical concerns. Additionally,
there are quantification challenges for example measuring pleasure and pain precisely is difficult and somewhat
relative. Consequently, the hedonic calculus faces practical limitations. Similarly, there are chances of unintended
consequences. Critics argue that focusing solely on outcomes may lead to unforeseen negative consequences.
However, the theory remains relevant for the subject at hand as utilitarianism would be discussed in light of its
moral philosophy which states that if the AI can generate 300 reports in less than a minute, the news crew;
particularly the editor, will be pleased based on the tenets of utilitarianism which promotes the greatest amount of
good for the greatest number of people (Driver, 2022).
Literature Review
Revolution, Adoption and Benefits of Robot Journalism
Have you ever asked Google for help or chatted with Alexa of Amazon or Cortana of Microsoft or inputted a key
word in Yahoo or Bing? Well, that generation of over a million results in less than a second is the work of Artificial
Intelligence, a software designed to collate data from across the web which is significantly altering the innovation
landscape, as entire industries incorporate AI into smart goods and automated systems (Graefe, 2016; Rieder &
Simon, 2015).
Companies worldwide are developing software solutions for generating automated news. In our increasingly
datafied societies, algorithms play an ever more important role. Private companies such as Google, Facebook or
Amazon use algorithmic operations to steer information flow, rank content, strategically place product ads, and
predict future user behaviour (Rieder & Simon, 2015).
Robot journalism has been conceptualised by different scholars as automated journalism, algorithmic journalism,
News Writing Bots as used by Monti (2019) interchangeably. While Linden (2017) calls it Software algorithms
which can generate news articles directly from structured data without human intervention. However, other scholars,
such as Wölker and Powell (2018), note that while the term "robot journalism" is dominantly used in media and
academia, the terminology is inaccurate. They note that it should be automated journalism since it deploys computer
algorithms, which, in comparison to robots, are not machines with "mechanical power."
While this distinction may be important, this study clarifies that automated journalism relies on software, and
algorithms either through physical robots or automated processes to performing tasks seamlessly. Therefore, for the
sake of this work it shall be used interchangeably as posited by Copeland (2024) that AI is the ability of a digital
computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.
In 2021, Dierickx confirmed that a huge number of newsroom tasks could be supported by automated journalism in
areas of production, inclusive of recognising the information required to extracting the data needed in conjunction
with verifying data, to producing stories that could include graphics which would literally have taken hundreds of
man-hours to accomplish. This will also explain why Shirky et al. (2013) situate innovations, such as automated
Journalism as a component of the post-industrial journalism which is intensifying the gap and effort that is required
in the routine of the newsroom and the culture of news writing.
Other astonishing features of automated news publishing include the sorting of the news stories, as the AI has
selection and prioritisation filters and when done, it automatically tags the articles (Dierickx, 2021; Graefe, 2016).
In 2014, The Los Angeles Times posted its earthquake story within three minutes of the disaster, making it the first
newspaper to publish the story (BBC News, 2014). This means reporting news around disasters, conflicts,
investigative journalism and long weeks of activities like sports, elections and rallies which are newsworthy can be
generated by AI, and the data can serve as a clue and a cue for the human journalists, showing them where they can
provide more details (Graefe, 2016). Human journalists can use the automatically generated stories as initial leads,
which can help them in exploring a particular case in more detail. Following the perceived attributes that determine
the rate of adoption as outlined by Rogers (2003) and Henderson (2017), robot journalism has a relative advantage
as it can operate in high-risk security areas (Granta, 2017), generate hundreds of stories in less than a minute

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
14
(Radcliffe, 2019). Graefe (2016) further summarises the relative advantage of auto journalism, as speed and volume,
reduced error can serve multiple audiences at the same time due to the NLG.
In 2022, ChatGPT became the latest chatbot from the Elon Musk-founded independent research body OpenAI
foundation. According to AIContentfy (2023) this is a notable development in the tech domain as ChatGPT is an AI
language model with the capacity to turn prompts into full stories. Additionally, FasterCapital (2024) notes that
ChatGPT has the potential to revolutionise the way news is generated, consumed, and interacted with. This is largely
due to its ability to transform prompts into complete narratives autonomously.
However, the question to note is to what degree is Robot Journalism adopted across newsroom?
Adoption of Robot Journalism Across Newsrooms
In Sweden, 19 Swedish newspapers, run by MittMedia Company, first adopted robot journalists in 2015 which
successfully produced sports and weather reports (Aljazairi, 2016). That same year, the New York Times executed
an experimental AI project known as Editor (Monti 2019; Underwood, 2019). The AI was to support the journalistic
process of drafting an article easily by inserting certain keywords that would generate the headline and important
elements of the story.
The year 2016 could be said to be the birth of AI in terms of launch, usage and adoption - Quartz received a
£193,000 grant from the Knight Foundation to set up a Bot Studio (Underwood, 2019). In 2018, the company
announced the launch of the Quartz AI Studio designed to support journalists’ use of machine learning to improve
the newsroom storytelling (Keefe, 2018).
The use of algorithms has been increasingly adopted in U.S. and Chinese newsrooms (Zheng et al., 2018). About
nine Chinese institutions have produced journalism-oriented automation products as at 2019, with the number
continuing to climb (Tencent, Alibaba, Baidu, Xinhua News Agency, Southern Metropolis Daily, Toutiao.com,
China Business Network, People's Daily, and China Science Daily), making China a major player in the world's
largest Internet market (Jia, 2020).
As at 2021, 14 robot journalism AI had been listed as top giants in a report published by Big Market Research. They
include Graphiq, Heliograf (Washington Post), Automated Insights, Yseop, Alphabet, Narrative Science, Arria,
Press Association, OnlyBoth, Bertie (Forbes), Cyborg (Bloomberg), Juicer (BBC), NewsWhip and Quartz Big
Market Research (2021). However, the world witnessed a revolution of AI in 2022 with the launch of ChatGPT
(Lock, 2022)
Algorithmic journalism is already a phenomenon in many newsrooms (Kotenidis & Veglis, 2021), and major news
organisations are in partnership with these tech companies adopting AI to produce news stories. Even at that, the
Chinese Internet market remains underexplored and, to some extent, has been isolated from the global news market
(Jia 2020), as can be seen from the Big Market Research carried out in 2021. Some of these listings would also serve
as cases where automated journalism has been or is being employed.
The cutting-edge technology was employed during the Rio 2016 Olympics game where news agencies tried out their
AI. The Washington Post was employing Heliograf which was designed to enhance storytelling for large-scale and
data-driven coverage of major news events. WP had earlier announced that it would be employing advanced
technology to auto-populate events schedules, results, medal tallies and more (WashPostPR, 2016). The technology
was first introduced during the 2016 Rio Olympics to assist journalists with reporting the results of medal events.
That same event also had Toutiao.com, an online news portal in China, publish more than 450 automated sports
news stories that were read by nearly one million readers (Jia, 2020; Shen, 2018). Some 7,266 miles away, the
Washington Post was updating the Heliograf software to expand the newsroom’s coverage of the US 2016 election.
This supported a coverage of more than five hundred races on the election night. To correlate information quickly
and efficiently, the Washington Post adopted the use of another AI known as Knowledge Map (Underwood 2019).
In the aforementioned year, Reuters announced the launch of their AI for generating business news, sports,
entertainment and more, through their collaboration with Graphiq, a corporation that specialises in semantic
technology (Reuters, 2016). Developing an AI by Graphiq was to automate the creation of simple graphics to

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
15
accompany relevant, human-reported news content (Kolodny, 2016). In an article in 2016, the Associated Press was
reported to have also employed the services of Graphiq to bring to life facts and figures for readers (Rodriguez,
2016). News Tracer is also a software employed by Reuters to source and collate breaking news (Underwood 2019).
Two years after the Olympics games coverage by Toutiao.com in China, the official state-run press agency of the
People's Republic of China known as Xinhua News Agency was able to generate 37,581 pieces of automated news
content on the 2018 FIFA World Cup, through an artificial intelligence (AI) product named MAGIC in collaboration
with the e-commerce giant Alibaba. This event fetched the agency approximately 116 million online page views (Fu
et al., 2019). That same year, the news agency (Xinhua) took automation a notch higher when it launched the
world's first artificial intelligence (AI) news anchor which could read sentences as naturally as a professional news
presenter (Xinhua, 2018).
Jung, et al., (2017) in a study, reports how one of the largest Chinese Internet companies Tencent, published the first
piece of Chinese automated news consisting of 1000 words written in 60 seconds, using an algorithm called
"Dreamwriter" in 2015 (Jung et.al., 2017; Polydor et.al., 2020) with 1000 words in 60 seconds.
Automated Insights is an artificial intelligence (AI) firm that creates programmes that can comprehend quantitative,
mathematical, and graphed data and display them in a coherent written style (Daley, 2021). The Associated Press
has adopted artificial intelligence to create publishable material by quickly recapping raw data from quarterly
corporate earnings, and turning them into readable stories (Miller, 2015). One such artificial intelligence is known as
Wordsmith, which is also used by Yahoo Sports! to produce personalised recaps and reports for fantasy football
players (Automated Insights, 2021). The Associated Press estimates that automated stories have saved up 20% of the
time journalists spent on earning reports, as well as enabling them to cover firms they previously could not cover
(Peiser, 2019). NewsWhip has also been used by the Associated Press newsroom in order to stay on top of trending
news stories on social networking sites, such as Twitter, Facebook, Pinterest, and LinkedIn (NewsWhip, 2016;
Underwood, 2019).
Juicer (BBC), in order to streamline workflows within the newsroom, BBC launched the ‘Juicer’, an AI that takes
news items, automatically qualifies them, then provides a fully featured API to access these contents and data. This
enables journalists to focus on what they do best, instead of mundane routine tasks (Dierickx, 2021).
In 2010, another Chicago-situated tech start-up called Narrative Science, began automating data-driven articles such
as sports stories. To bridge the data-driven gap, an AI called Quill was designed to allow people to build,
comprehend and transmit data in intelligent story-like ways (Dale, 2020; Narrative Science, 2021).
Forbes has a Content Management System (CMS) called Bertie, which was designed specifically for its in-house
newsroom (Michalski, 2021) to digitally cover high impact, expertly authored stories across its seven channels
through the use of integrated video, graphical imagery, data visualisation for a detailed story generation (Zalatimo,
2018).
In the most recent AI development launched in November 2022, OpenAI's AI-powered chatbot, ChatGPT, has
sparked discussions among journalists regarding its potential impact on the news industry (Adami, 2023). As a
highly advanced language model, ChatGPT has the capability to generate coherent and contextually relevant
responses to a wide range of queries, making it a valuable tool for content creation and interaction.
While these agencies are transitioning towards AI, in contrast a survey results from Muck Rack, a company
specialising in public relations management notes that only 28% of journalists currently incorporate AI into their
workflow. This suggests that a significant portion of journalists are already utilizing AI tools in various aspects of
their work, such as content generation, data analysis, or audience engagement. Furthermore, the survey also reveals
that a smaller percentage, specifically 20%, are planning to explore the use of AI in the future. This indicates that
there is a segment of journalists who have not yet adopted AI technology but are open to the idea of incorporating it
into their workflow in the near future (Business Wire, 2024).
Time would not permit the researchers to delve into Yseop, Alphabet, Arria, Press Association, OnlyBoth and
Cyborg (Bloomberg). From the adoption of robot journalism in relation to the perceived attributes in adoption of an

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
16
innovation, the complexity of automated journalism should not be a problem but the emergence of increasingly
powerful tools, such automated journalism should serve as a handwriting on the wall, to signal that it is time for
journalists to learn the skills necessary to take advantage of AI and increase their creativity (Dierickx, 2021).
The Quandary and Ethical Issues Facing Robot Journalism
Pavlik in 2013 writes on guaranteeing the adoption of an innovation, and like the utilitarian theory, cites four
principles to guide the usage: Intelligence or research; a commitment to freedom of speech, a dedication to the
pursuit of truth and accuracy in reporting, and ethics (Pavlik, 2013). While the growing ability of machine-written
news texts portends new possibilities for an expansive terrain of news content far exceeding the production
capabilities of human journalists, it does not serve itself, as people are needed to evaluate, edit or correct an
algorithm’s work. (Lohr, 2013). While collaborating with Graphiq in 2016, Brian Carovillano, the Associated Press'
Vice President of US news had to reassure the public about the AI's veracity, saying, “AP has pretty strict editorial
standards around accuracy; we spent quite a bit of time with [Graphiq] just making sure that it met all those
standards, and it has been really good” (Rodriguez, 2016). This is crucial because no media organisation likes to be
found wanting for inaccuracies that could lead to libellous suits. Automated Journalism faces areas of doubts for
accuracy. For example, some AIs, such as the Microsoft Word Researcher source finder, which is a search engine is
not extensive and most times, generates results that are not tied to the key words.
In the legal world, there are two major concerns to consider: ownership and liability. According to a study
performed by Lewis, et al. (2019), the rise of automated journalism presents a number of possibilities and risks for
news organisations through libellous publications, and the answer to warding off such ethical pitfalls is to
incorporate human strengths so that everyone can take part in the improvement of AI (Bäck et al., 2019). This is
because no technology can operate without human effort (Moniz, 2013), it would be like a horse without a rider.
The rise of automated journalism presents a range of potential and also pitfalls for news organisations such as
defamation (Lewis et al., 2019). This is because the editorial judgement of the human is required, as robots hold no
emotions or apply ethics that have not been encoded in them. Furthermore, AIContentfy team (2023) notes that
misinformation and disinformation are indeed pressing issues in contemporary society, exacerbated by the rapid
dissemination of information facilitated by digital technologies and social media platforms. The emergence of large
language models such as GPT-3 introduces both opportunities and challenges in addressing this issue.
A further dimension, is the fear that human journalists could lose their jobs over the presence of robots. This could
be true, as was reported in 2016 when Facebook fired the team which had been curating its “Trending Topics”
section and replaced them with an algorithm that would automatically recognise and promote popular topics (Lewis
et al., 2019). But two years later, Facebook had to apologise after the AI selected and featured a fake, sensational
news story about Fox News anchor, Megyn Kelly in its influential “Trending” topics section (Gunaratna, 2016).
This was a case of libel because user-generated content (UGC) as a source of information for journalists needs
support for automated methods to detect, filter, contextualise and verify citizen reports of breaking news events
(Heravi et al., 2014) to avoid reporting inaccuracies. But who was to be held accountable, Facebook or the Bot? In
the case of authorship, experts agree that algorithms cannot be held accountable for errors, therefore liability for
automated content will rest with a natural person, which means the journalist, editor and the publishing agency
(Aljazairi, 2016; Graefe, 2016; Montal & Reich, 2017). This makes it a key factor for consideration for its
compatibility as perceived attribute to adopting automated journalism but does not undermine its relative advantage
of safety for the journalist in terms of going to natural disaster sites, war zones or pandemic regions.
Will robots take over jobs? In a study by Thurman, Dörr and Kunert (2017), the authors outline the limitations of
automation around the nature of its sources, while Monti (2019) explains that though the robot journalist could
chronicle events to make up a story, it lacks the ability to critique and dig deeper. This supports a finding where half
of the experts who responded to a survey in 2014 expected that technology will not displace more jobs than it
creates by 2025, and this agrees with Graefe (2016), that journalists who want to maintain their jobs should develop
skills that the algorithms cannot perform. Additionally, a report by Adami, (2023) suggests that while generative AI
like ChatGPT has potential applications in supporting journalistic tasks, its current limitations in originality,
breaking news capabilities, analytical skills, and voice constrain its ability to assume a more significant role in

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
17
journalism. As AI technology continues to evolve, addressing these limitations may be crucial for realizing its full
potential in the field of journalism.
However, the aforementioned are contrary to what Linden (2017) posits in a study where automation of journalism
routine tasks is said to be resulting in loss of jobs for journalists as three years after the article in 2020, Microsoft did
relieve twenty-seven (27) journalists of their jobs, replacing them with AI, making the loss of employment for
journalists a major concern (Caswell & Dörr, 2018; Montal & Reich, 2017). This concurs with Graefe’s submission
in 2016 that “Automated journalism will likely replace journalists who merely cover routine topics but will also
generate new jobs within the development of news-generating algorithms.”
In another study, Dierickx (2021) opines that one of the major hindrances to the development of automated
journalism lies in cultural oppositions linked to fears regarding job loss and different work habits, and sometimes
even general hostility to technology. In looking at the diffusion of innovation theory, this factor would lead to late
majority adoption of automated journalism by journalists. But since the whole idea behind robotic journalism is to
liberate journalists from their routine tasks which are commonplace reports, repetitive action of generating
earnings/weather/sports/disaster reports, the solution is presented in Rogers (2003) as trialability. This is by allowing
the organisation to experiment with the innovation and find ways to improve it to fit their audience too. In this case,
the journalists would be needed to prescribe the specific sources from which robots should glean their data to
prevent them from including irrelevant sources too.
While the ethical dilemma is still being debated, some authors have also raised concerns over the participatory
nature of AI for example in immersive journalism. Okocha and Akpe (2024) note that ethics in automated
journalism within the context of immersive storytelling involves recognising and addressing the unique
responsibilities that journalists have towards their audience members.
AI Peregrination and Next Steps
This study has been able to explore how the routine of the orthodox medium for mass communication has been
disrupted by AI and translated to a more personal media as more information is stored on more social media, which
is increasing the ability to share the information accurately to more newsrooms and audience with the NLG.
Automated journalism AI is making man-hour tasks much easier in the field of news development.
The ban on social media platforms may not affect automated journalism as long as news agencies, such as Xinhua in
China AI are backed up by the governments that want to promote their media agency. Ethical issues arising are
around the fabrics that make up good journalism around honesty, fairness and accuracy but these have been
performed well by the robot journalists discussed.
Whether human journalists will lose their jobs to robots, this angle is quite clear as credibility of source and
detecting a phoney news article still needs to be vetted by the human journalist (Underwood, 2019). To continue to
discover word patterns that could enhance scripts and news contents, calls for software upgrade consistently with the
aid of humans. To stay afloat across the globe, newshounds need to keep pace with the evolution of new
technologies through training and retraining, as one thing remains clear; journalists and editors will still be needed in
the newsroom to detect and eliminate faux news which the AI cannot completely tell (White, 2020).
Journalism is the technique or procedure of news production which involves gathering, assessing, creating and
dissemination (American Press Institute, 2021). Construed on facts and supported with proof, journalism
encompasses both the profession and collaborative media, such as what we can read (print), what we can see and
hear (television), what we can hear (radio), and the internet, which is seeing the introduction of new technologies
and techniques that are transforming the way journalism is done (Haak et al., 2012). Journalism is guided by
professional code of ethics which are the mutual values that guide news production and which should be followed to
execute the job responsibly (Woodward, 2020). These professional and ethical standards are an important tool for
journalists to checkmate themselves according to (Society of Professional Journalists, 2014) and have been upheld
across the world.
Additionally, journalists employing automated journalism techniques must consider how AI-generated content may
impact audience perceptions, emotions, and decision-making processes. Therefore, journalists can uphold principles

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
18
of integrity, accountability, and respect for audience autonomy, by navigating the ethical complexities of immersive
journalism and AI-driven content creation, to foster trust and credibility in the digital news landscape.
Juridically, the robot is not liable for libellous news stories or mistakes made, this means the editor needs to still
perform the gatekeeping function to maintain a balance and ensure tenability of source, integrity, and privacy of
source. Around authorship, the time, energy, and money spent in creating and employing automated journalism
should be accorded to parties involved just like an end credit in a movie. One must realise and acknowledge that the
digital camera does not roll itself; also, the information cannot examine itself. This serves as a checkmating tool for
the organisations adopting the technology.
Conclusion
In summary, technology metamorphosis within the media enterprise in view of automation have made the recent
decade of journalism in the press field quite interesting from news sourcing, editing, production and publishing. The
literature review above shows that the news outlook will be forged by the adoption of robot journalism. The
deciding factor will be how soon media professionals embrace this and with support from the countries they perform
their functions.
The way journalists are receiving information also affects the way news is consumed. The use of AI in news
generation makes things easier and will allow human journalists to focus on more complex events. The exposé on
how automated journalism has been adopted in the U.S, China and Sweden goes to show how the tech is giving
room for the right angle of critic critiquing during reporting, and serve as a guide for deeper and thorough
investigative reports. The usage of automated journalism shows that it is a practical system that works by saving
time, energy and cost.
For developing countries and growing online news firms particularly in Africa, it means media managers need to
adopt new tools required to improve their work outputs in order to meet the growing demands. From the coverage of
the earthquake by the Los Angeles Times in 2014, it goes to show how automated journalism will protect journalists
across the world in times of pandemics, wars, disasters and life-threatening scenarios.
AI development the world over, is an enterprise opportunity as stated in the report by Big Market Research(2021)
that studied vital factors about the Automated Journalism Market, which has also added a new dimension to its vast
depository titled: “Global Automated Journalism Market”. In a report about Automated Journalism Market, the
agency listed Key Players in 2021 categorising them by types, applications, countries and market size with a
Forecast to 2024. The report included profiles of leading players who have been discussed above, such as Graphiq,
Heliograf (Washington Post), Automated Insights, Narrative Science, Bertie (Forbes), NewsWhip and Quartz.
It is safe to say, that the introduction of AIs like ChatGPT represents a significant milestone in the evolution of AI-
powered language models. Its capacity to generate full stories from prompts not only showcases the advancements
in AI technology but also opens up new possibilities for content creation and journalism. As ChatGPT continues to
evolve and integrate into various sectors, its impact on the way information is generated, consumed, and interacted
with is likely to be profound, shaping the future of storytelling and media dissemination. In addition, the market
reports are designed to help industry experts and business owners know the estimated market size, market status,
future development, growth opportunity, challenges and growth drivers of automated journalism by analysing the
historical overall data of the considered market segments.
These further justify the discussion from the diffusion of innovation theory, that innovations are not adopted by all
individuals in a social system at the same time but within a time sequence; and just like the proliferation of new
media, one can see how the convergence of digital media tools has also revamped the way news is consumed
globally; and with what is now called mobile journalism. Robot journalism has a relative advantage over the human
ability currently in terms of speed and volume of data generation, which should also be a consideration for adoption.
Consequently, automated journalism is creating more jobs for algorithm coders therefore, it is a field of information
and communication technology that should be included in the mass communication departments for upcoming
journalists to be conversant with how communication tools work, and not should be limited to audio, TV and print
production. This will ensure they stay employable in the changing field of journalism.

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
19
This study posits that the press field is already charting a course across the globe towards adopting robot journalism
for a stress-free tomorrow; and just like with other man-made products, automated journalism is still being upgraded
continuously, as with every other software similar to our mobile phone ‘apps’.
References
Van der Haak, B., Parks, M., & Castells, M. (2012). The future of journalism: Networked journalism. International
Journal of Communication, 6, 16. https://ijoc.org/index.php/ijoc/article/view/1750/832.
Adami, M. (2023, March 23). Is ChatGPT a threat or an opportunity for journalism? Five AI experts weigh in.
Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/news/chatgpt-threat-or-
opportunity-journalism-five-ai-experts-weigh
AIContentfy team. (2023, January 25). Ethical considerations of ChatGPT and AI. AIContentfy.
https://aicontentfy.com/en/blog/ethical-considerations-of-chatgpt-and-ai
Akinfemisoye, M. O. (2014). Negotiating Convergence: “Alternative” journalism and institutional practices of
Nigerian journalists. Digital Technologies and the Evolving African Newsroom, 2(1), 62–76.
https://doi.org/10.1080/21670811.2013.850201
Aljazairi, S. (2016, January 1). Robot journalism: Threat or an opportunity. DIVA.
http://oru.diva-portal.org/smash/record.jsf?pid=diva2:938024
Antonopoulou, P., & Kyriazis, A. (2018). Potential Applications of Algorithmic (Robot) Journalism for the Greek
Sport Media. New Media and Mass Communication, 67, 27–30.
https://www.iiste.org/Journals/index.php/NMMC/article/view/41242/42404.
Automated Insights. (2021). Yahoo! Automated Insights. https://automatedinsights.com/customer-stories/yahoo/
Bäck, A., Diakopoulos, N., Granroth-Wilding, M., Haapanen, L., Leppänen, L., Melin, M., Moring, T., Munezero,
M., Sirén-Heikel, S., Södergård, C., & Toivonen, H. (2019). News Automation The rewards, risks and
realities of “machine journalism” (C.-G. Lindén & H. Tuulonen, Eds.; pp. 4–55). WAN-IFRA.
https://www.researchgate.net/publication/333753590_News_Automation_The_rewards_risks_and_realities
_of_’machine_journalism’
Baylé, M. (2019, March 15). Ethical dilemmas of AI: Fairness, transparency, collaboration, trust, accountability &
morality. UX Collective. https://uxdesign.cc/ethical-dilemmas-of-ai-fairness-transparency-human-machine-
collaboration-trust-accountability-1fe9fc0ffff3
BBC News. (2014, March 18). Robot writes LA Times earthquake breaking news article. BBC News.
https://www.bbc.com/news/technology-26614051
Big Market Research. (2021). Automated journalism market report 2021 by key players, types, applications,
countries,
market size, forecast to 2024 (Based on 2021 COVID-19 Worldwide Spread). The Courier.
Business Wire. (2024, March 12). Muck rack survey: 28% of journalists are using AI; 20% plan to explore it. Yahoo
Finance. https://finance.yahoo.com/news/muck-rack-survey-28-journalists
Cambridge Dictionary. (2024). public trust. https://dictionary.cambridge.org/dictionary/english/public-trust
Carlson, M. (2015). The robotic reporter - Automated journalism and the redefinition of labor, compositional forms,

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
20
and journalistic authority. Digital Journalism, 3(3), 416–431.
https://doi.org/10.1080/21670811.2014.976412
Caswell, D. (2018). Automated Journalism 2.0: Event-driven narratives - From simple descriptions to real stories.
Journalism Practice, 12(4), 477–496.
Caswell, D., & Dörr, K. (2018). Automated Journalism 2.0: Event-driven narratives - From simple descriptions to
real
stories. Journalism Practice, 12(4), 477–496. https://doi.org/10.1080/17512786.2017.1320773
Dale, R. (2020). Natural language generation: The commercial state of the art in 2020. Natural Language
Engineering,
26(4), 481–487. https://doi.org/10.1017/s135132492000025x
Daley, S. (2021, August 9). 23 artificial intelligence examples shaking up business across industries. Built In.
https://builtin.com/artificial-intelligence/examples-ai-in-industry
Damanpour, F. (1996). Organizational complexity and innovation: Developing and testing multiple contingency
models. Management Science, 42(5), 693–716. https://doi.org/10.1287/mnsc.42.5.693
Dierickx, L. (2021, April 6). Artificial intelligence and journalism: A race with machines. Equal Times.
https://www.equaltimes.org/artificial-intelligence-and?lang=en
Dörr, K. N. (2016). Mapping the field of Algorithmic Journalism. Digital Journalism, 4(6), 700–722.
https://doi.org/10.1080/21670811.2015.1096748
Downs, G. W. (1976). Conceptual issues in the study of innovation /. Administrative Science Quarterly, 12, 700–
714.
http://eduq.info/xmlui/handle/11515/16883.
Driver, J. (2022). The History of Utilitarianism. Stanford Encyclopedia of Philosophy Archive.
https://plato.stanford.edu/archives/win2022/entries/utilitarianism-history.
Encyclopaedia Britannica. (2024, March 15). Trust. Britannica Money.
https://www.britannica.com/money/trust-law#ref269750
Evans, R. (2023, June 22). Understanding Utilitarianism: A Guide. Philosophos.Org.
https://www.philosophos.org/ethics-utilitarianism
Faggella, D. (2018). Yahoo! Uses NLG to deliver personal fantasy sports recaps and updates. In Emerj.
https://emerj.com/ai-case-studies/yahoo-uses-nlp-deliver-personal-fantasy-sports-recaps-updates/
Fairley, R. (2014, August 21). The rapid evolution of the consumer camcorder. Videomaker.
https://www.videomaker.com/article/f22/17178-the-rapid-evolution-of-the-consumer-camcorder/
FasterCapital. (2024, March 15). ChatGPT and future of news automation and ai in journalism. FasterCapital.
https://fastercapital.com/content/Chatgpt-and-future-of-news-automation-and-ai-in-journalism.html
Granger, J. (2019, July 16). App for journalists: Voice Record Pro, for transcribing audio interviews.
Journalism.Co.Uk. https://www.journalism.co.uk/news/app-for-journalists-voice-record-pro-for-
transcribing-audio-interviews/s2/a741784/

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
21
Granta. (2017, December 11). Advantages and disadvantages of robotic automation. Granta Automation |
Automated
Palletising Solutions. https://www.granta-automation.co.uk/news/advantages-and-disadvantages-of-robotic-
automation/
Gruenbaum, R. (2015). Critical mass. In Making Social Technologies Work. Palgrave Macmillan.
https://link.springer.com/chapter/10.1057/9781137024824_21
Gunaratna, S. (2016, August 29). Facebook apologizes for promoting false story on Megyn Kelly in #Trending. CBS
News. https://www.cbsnews.com/news/facebooks-trending-fail-news-section-reportedly-highlights-fake-
news-on-megyn-kelly/
Heravi, B. R., Morrison, D., Khare, P., & Marchand-Maillet, S. (2014). Where is the news breaking? Towards a
location-based event detection framework for journalists. In C. Gurrin, F. Hopfgartner, W. Hurst, H.
Johansen, H. Lee, & N. O’Connor (Eds.), International Conference on Multimedia Modeling 2024 (Vol.
8326, pp. 192–204). Springer. https://link.springer.com/chapter/10.1007/978-3-319-04117-9_18
Jia, C. (2020). Chinese automated journalism: A comparison between expectations and perceived quality.
International Journal of Communication, 14(0). https://ijoc.org/index.php/ijoc/article/view/13334.
Jung, J., Song, H., Kim, Y., Im, H., & Oh, S. (2017). Intrusion of software robots into journalism: The public’s and
journalists’ perceptions of news written by algorithms and human journalists. Computers in Human
Behavior, 71, 291–298. https://doi.org/10.1016/j.chb.2017.02.022
Keefe, J. (2018, November 20). Announcing the Quartz AI Studio, designed to help journalists use machine
learning.
Quartz. https://qz.com/1464390/announcing-the-quartz-ai-studio-designed-to-help-journalists-use-machine-
learning
Kolodny, L. (2016, September 13). Reuters is the latest large news agency to embrace content automation.
TechCrunch. https://techcrunch.com/2016/09/13/reuters-is-the-latest-large-news-agency-to-embrace-
content-automation/
Kotenidis, E., & Veglis, A. (2021). Algorithmic journalism—current applications and future perspectives.
Journalism
and Media, 2(2), 244–257. https://doi.org/10.3390/journalmedia2020014
Leigh, Joshua. (2017, November 16). The evolution of the media landscape (and why you probably won’t finish this
article). Method Perspectives. https://medium.com/method-perspectives/the-evolution-of-the-media-
landscape-and-why-you-prosbably-wont-finish-this-article-df055ce529c7
Lewis, S. C., Sanders, A. K., & Carmody, C. (2019). Libel by algorithm? Automated journalism and the threat of
legal liability. Journalism & Mass Communication Quarterly, 96(1), 60–81.
https://doi.org/10.1177/1077699018755983
Lindén, C.-G. (2017). Algorithms for journalism: The future of news work. The Journal of Media Innovations, 4(1),
60–76. https://doi.org/10.5617/jmi.v4i1.2420
Lock, S. (2022, December 5). What is AI chatbot phenomenon ChatGPT and could it replace humans? The
Guardian.

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
22
https://www.theguardian.com/technology/2022/dec/05/what-is-ai-chatbot-phenomenon-chatgpt-and-could-
it-replace-humans
Lohr, S. (2013, March 11). Algorithms Get a Human Hand in Steering Web. The New York Times.
https://www.nytimes.com/2013/03/11/technology/computer-algorithms-rely-increasingly-on-human-
helpers.html
Lopez, J. (2015, June 29). Types of innovation. Constant Contact Tech Blog.
https://techblog.constantcontact.com/software-development/types-of-innovation/
Michalski, A. (2021, June 1). Content management systems (CMS) aren’t boring – take it from me. Forbes.
https://www.forbes.com/sites/forbesdigitalgroup/2021/06/01/content-management-systems-are-not-boring-
the-what-why-and-how/
Miller, R. (2015, January 29). AP’s “robot journalists” are writing their own stories now. The Verge.
https://www.theverge.com/2015/1/29/7939067/ap-journalism-automation-robots-financial-reporting
Monique. (2023, September 13). 8 types of application software. Ubique Digital Solutions.
https://ubiquedigitalsolutions.com/blog/8-types-of-application-software/
Moniz, A. B. (2013). Robots and humans as co-workers? The human-centred perspective of work with autonomous
systems. . IET Working Papers Series, 3. https://ideas.repec.org/p/ieu/wpaper/51.html.
Montal, T. (2017). I, Robot. You, Journalist. Who is the Author? Digital Journalism, 5(7), 829–849.
https://doi.org/10.1080/21670811.2016.1209083
Monti, M. (2019). Automated journalism and freedom of information: Ethical and juridical problems related to AI in
the press field. SSRN, 1.
Narrative Science. (2021). Quill and augmented analytics. https://narrativescience.com/our-story
Newman, N. (2021, January 7). Journalism, media, and technology trends and predictions 2021. Reuters Institute
for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/journalism-media-and-technology-trends-
and-predictions-2021
News, B. (2014, March 18). Robot writes LA Times earthquake breaking news article. BBC News.
https://www.bbc.com/news/technology-26614051
NewsWhip. (2016, April 19). How the associated press use newswhip to deliver news. Newswhip.
https://www.newswhip.com/2016/04/guest-post-associated-press-use-newswhip/
OECD. (2020). The territorial impact of COVID-19: Managing the crisis across levels of government. In OECD
Policy
Responses to Coronavirus (COVID-19). Organisation for Economic Co-Operation and Development
(OECD). http://dx.doi.org/10.1787/d3e314e1-en
Okocha, D. O. & Akpe, S. (2024). 360-Degree Journalism: Ethics and Challenges of Immersive Storytelling in
Africa. In T. Dodds (Ed.), Immersive journalism: Virtual reality and the future of the news industry (pp. 81–104).
Rowman & Littlefield. https://rowman.com/ISBN/9781666938609/Immersive-Journalism-Virtual-Reality-
and-the-Future-of-the-News-Industry

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
23
Olasunkanmi, I. (2020, August 25). Nigerian university students find online learning painful: Here’s why. The
Conversation. https://theconversation.com/nigerian-university-students-find-online-learning-painful-heres-
why-143919
OpenPR. (2023, January 3). Automated journalism market is booming worldwide. OpenPR.
https://www.openpr.com/news/2869930/automated-journalism-market-is-booming-worldwide-major-giants
Pavlik, J. V. (2013). Innovation and the future of journalism. Digital Journalism, 1(2), 181–193.
https://doi.org/10.1080/21670811.2012.756666
Peiser, J. (2019, February 5). The Rise of the Robot reporter. The New York Times.
https://www.nytimes.com/2019/02/05/business/media/artificial-intelligence-journalism-robots.html
Plsek, P. E., & Greenhalgh, T. (2001). Complexity science: The challenge of complexity in health care. BMJ
(Clinical
Research Ed.), 323(7313), 625–628. https://doi.org/10.1136/bmj.323.7313.625
Polydor, S., Czapska, M., & Roberts, K. (2020, April 20). Chinese dreamwriter decision: A dream come true for ai-
generated works? Connect On Tech. https://www.connectontech.com/chinese-dreamwriter-decision-a-
dream-come-true-for-ai-generated-works/
Press Emblem. (2024). Countries with the most Covid-19 related journalist deaths. Press Emblem.
https://pressemblem.ch/-1.shtml
Press Emblem Campaign. (2021). At least 1580 journalists dead from Covid-19.https://www.pressemblem.ch/pec-
news
Radcliffe, D. (2019, June 2). Seven ways robots are being used by publishers and newsrooms. Medium.
https://medium.com/damian-radcliffe/seven-ways-robots-are-being-used-by-publishers-and-newsrooms-
22fc67b87761
Reuters. (2016, August 30). Reuters and Graphiq deliver interactive data visualizations to publishers. Thomson
Reuters. https://www.thomsonreuters.com/en/press-releases/2016/august/reuters-graphiq-interactive-data-
visualizations.html
Rieder, G., & Simon, J. (2015, June 19). Big data, corporate governance, and the limits of algorithmic
accountability.
Proceedings of ISIS Summit Vienna 2015—The Information Society at the Crossroads.
http://dx.doi.org/10.3390/isis-summit-vienna-2015-t3.3001
Rodriguez, S. (2016, October 18). Meet the A.I. Startup that’s whipping up infographics for thousands of
newspapers
in the U.S. Inc.Africa. https://www.incafrica.com/library/salvador-rodriguez-graphiq-ap-visualizations
Seng, J. K. P., Ang, K. L., Peter, E., & Mmonyi, A. (2022). Artificial intelligence (AI) and machine learning for
multimedia and edge information processing. Electronics, 11(14), 2239.
https://doi.org/10.3390/electronics11142239
Shen, X. (2018, October 2). Breaking down China’s most popular news app, Toutiao. South China Morning Post.
https://www.scmp.com/abacus/who-what/what/article/3028243/breaking-down-chinas-most-popular-news-
app-toutiao

Journal of Media, Communication &
Languages Vol. 9, 1 March, 2024
24
Shirky, C., Bell, E., & Anderson, C. W. (2013). Post-Industrial journalism. NYU Scholars.
https://nyuscholars.nyu.edu/en/publications/post-industrial-journalism
Society of Professional Journalists. (2014, September 6). SPJ Code of Ethics. Society of Professional Journalists.
https://www.spj.org/ethicscode.asp
Tardi, C. (2023, December 20). Utilitarianism: What it is, founders, and main principles. Investopedia.
https://www.investopedia.com/terms/u/utilitarianism.asp
Thurman, N., Dörr, K., & Kunert, J. (2017). When reporters get hands-on with robo-writing. Digital Journalism,
5(10), 1240–1259. https://doi.org/10.1080/21670811.2017.1289819
Underwood, C. (2017, November 17). Automated journalism – AI applications at New York Times, Reuters, and
other
media giants. Emerj. https://emerj.com/ai-sector-overviews/automated-journalism-applications/
WashPostPR. (2016, August 5). The Washington Post experiments with automated storytelling to help power 2016
Rio Olympics coverage. The Washington Post. https://www.washingtonpost.com/pr/wp/2016/08/05/the-
washington-post-experiments-with-automated-storytelling-to-help-power-2016-rio-olympics-coverage/
West, H. R., & Duignan, B. (2024, March 29). Utilitarianism. Encyclopedia Britannica.
https://www.britannica.com/topic/utilitarianism-philosophy
Westacott, E. (2019, July 26). What are the three basic principles of utilitarianism? ThoughtCo.
https://www.thoughtco.com/basic-principles-of-utilitarianism-3862064
White, P. (2020, May 16). Covid has been hard on journalism. AI can help — without taking your job. ThePrint.
https://theprint.in/world/covid-has-been-hard-on-journalism-ai-can-help-without-taking-your-job/422765/
Woodward, B. (2021, September 30). What is ethics in journalism? Learn about journalism ethics with tips from
legendary journalist Bob Woodward - 2024. MasterClass. https://www.masterclass.com/articles/what-is-
ethics-in-journalism-learn-about-journalism-ethics-with-tips-from-legendary-journalist-bob-woodward
Xinhua. (2018, November 8). World’s first AI news anchor makes “his” China debut - Xinhua. English.News.Cn.
http://www.xinhuanet.com/english/2018-11/08/c_137591813.htm
Zalatimo, S. (2018, July 11). Entering the next century with A new Forbes experience. Forbes.
https://www.forbes.com/sites/forbesproductgroup/2018/07/11/entering-the-next-century-with-a-new-
forbes-experience/?sh=7fe3c23b3bf4
Zheng, Y., Zhong, B., & Yang, F. (2018). When algorithms meet journalism: The user perception to automated
news in a cross-cultural context. Computers in Human Behavior, 86, 266–275.
https://doi.org/10.1016/j.chb.2018.04.046