Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem

Abstract

The Associated Press presents original research on the state of generative AI usage and views in a survey conducted with journalists around the world.

Full text

Generative AI
in Journalism:
The Evolution of
Newswork and Ethics
in a Generative
Information Ecosystem
Authors:
Nicholas Diakopoulos, Hannes Cools, Charlotte Li,
Natali Helberger, Ernest Kung, Aimee Rinehart
Edited by:
Lisa Gibbs
April 2024

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 20242
Table of Contents
Introduction 03
The Future of Newswork 09
Ethical Considerations 22
Looking Ahead 34
Appendix 37

Introduction

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 20244
1 ChatGPT is growing faster than TikTok. CBS News. Feb 1, 2023.
https://www.cbsnews.com/news/chatgpt-chatbot-tiktok-ai-artificial-intelligence
The introduction of ChatGPT by OpenAI in late 2022 captured
the imagination of the public—and the news industry—with
the potential of generative AI to upend how people create and
consume media. Generative AI is a type of artificial intelligence
technology that can create new content, such as text, images,
audio, video, or other media, based on the data it has been trained
on and according to written prompts provided by users. ChatGPT
is the chat-based user interface that made the power and
potential of generative AI salient to a wide audience, reaching
100 million users within two months of its launch1.
Other big tech companies quickly flocked to compete with their
own AI models: Bard and then Gemini from Google, Claude from
Anthropic, Copilot from Microsoft, and open source offerings
like LLaMA from Meta, not to mention new search products
like Perplexity, browser experiences like Arc, or the creation of
interfaces like Adobe’s Firefly and Photoshop that integrate the
technology and transform how end-users create or interface with
information. Although versions of the tech have been around since
2018, by late 2022 it was suddenly working (sort of), spurring its
integration into various products and presenting not only a host
of opportunities for productivity and new experiences but
also some serious concerns about accuracy, provenance and
attribution of source information, and the increased potential
for creating misinformation.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 20245
2 https://generative-ai-newsroom.com
3 David Caswell. Rising to the Challenge: Applying Generative AI in Newsrooms. Generative AI
in the Newsroom (October, 2023).
https://generative-ai-newsroom.com/rising-to-the-challenge-applying-generative-ai-in-newsrooms-283d5bb3de53
4 Jessica Cecil. 2023 round tables on AI and the global news industry. Reuters Institute.
https://reutersinstitute.politics.ox.ac.uk/news/2023-round-tables-ai-and-global-news-industry
5 Charlie Beckett and Mira Yaseen. Generating Change. JournalismAI.
https://www.journalismai.info/research/2023-generating-change
6 Teemu Henriksson. New survey finds half of newsrooms use Generative AI tools; only 20% have guide
lines in place. WAN-IFRA. https://wan-ifra.org/2023/05/new-genai-survey
7 Local News AI: Building tools and training newsrooms. https://ai.ap.org
Throughout 2023 the news industry scrambled to figure out what all this new technology
would mean for news gathering, production and distribution practices, products and user
experiences, for their already precarious business models, and the value of their intellectual
property. Understanding how audiences might interact with and consume information in the
future is again being challenged, after the disruption that social media wrought. Initiatives
like the Generative AI in the Newsroom (GAIN) project2, AI, Media and Democracy Lab, the
Open Society Foundation AI in Journalism Challenge3, The Reuters Journalism Institute
roundtables on generative AI4, and the LSE’s JournalismAI survey in mid-20235, as well as
others, including an early survey from WAN-IFRA6 and the AP’s own convenings7 have all
contributed to advancing the industry’s understanding of the technology and what it might
mean for journalism.
This report serves as a snapshot of how the industry has grappled with the initial promises
and challenges of generative AI toward the end of 2023. In this report, we present a survey of
292 individuals in the news industry about how they use and want to use generative AI and
what they see as the main ethical and practical issues around developing responsible usage.
We collected survey responses for three weeks, from December 4 to December 22, 2023,
with the AP circulating the survey among its email lists of news organization practitioners
and through various social media and Slack-group postings.
Fully 81.4% of respondents indicated that they were knowledgeable about generative AI (See
Figure 1), and 73.8% indicated that they or their organization had already used generative AI
in some capacity (See Figure 2). The average number of years worked in the news industry by
respondents was 18 years (See Appendix A). In other words, our sample reflects how some
of the more savvy and seasoned members of the profession are reacting to the technology.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 20246
Figure 1.
More than three
quarters of
respondents state
that they are
knowledgeable
about generative AI.
Figure 2.
When asked “Have you
or your organization
used generative AI
in some capacity?”
almost three quarters
of respondents answer
affirmatively.
Respondents who agree with the statement “I am knowledgable about generative AI.”
Respondents by prior usage of generative AI
Strongly Disagree
Yes
2.1%
73.8%
Disagree 3.8%
Neither Agree or Disagree
No
12.8%
26.2%
Strongly Agree 24.5%
Agree 56.9%
8 See: Women and leadership in the news media 2023: evidence from 12 markets. Reuters Institute. March 8, 2023.
https://reutersinstitute.politics.ox.ac.uk/women-and-leadership-news-media-2023-evidence-12-markets
The sample skews heavily toward people working in North America (61.7%) and Europe
(24.8%) with a smattering of respondents working in Asia (7.9%), Africa (2.8%), Oceania
(1.7%) or South America (1.0%). There was an over-representation of men responding
(58.3%) though this appears roughly consistent with expected base rates in the media
industry8. And while respondents in Editor roles dominated (34.5%), we also captured
responses from Executives (20%), Reporters (18.3%), Technologists (9.3%) and people in
other roles such as Product or in roles wearing multiple hats (17.9%). For more details on the
sample we collected, see Appendix A.

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Based on participants’ responses and our analysis, we find that generative AI is already
changing work structure and organization, even as it triggers ethical concerns around use.
Here are some of our key takeaways:
• Applications in News Production. The most predominant current use cases for
generative AI include various forms of textual content production, information gathering
and sensemaking, multimedia content production, and business uses. Respondents
expressed interest in expanding the use of generative AI for information gathering and
sensemaking, working with data, business uses, and metadata creation, with some
interest in exploring new user experiences with chatbots and through personalization.
Overall, attention is focused on improving and making existing workflows more efficient
with considerably less attention to exploring and innovating new experiences.
• Changing Work Structure and Organization. There are a host of new roles emerging
to grapple with the changes introduced by generative AI including for leadership,
editorial, product, legal, and engineering positions. Almost half of respondents indicated
that tasks or workflows have already changed because of generative AI. New work is
created in devising effective prompts and in editing outputs. Perceived efficiency gains
are variable and additional research is needed to evaluate any real performance gains
across a range of common tasks. Overall, these findings underscore the need for training
initiatives and for more fine-grained evaluations to measure actual shifts in productivity.
• Work Redesign. There is an unmet opportunity to design new interfaces to support
journalistic work with generative AI, in particular to enable the human oversight needed
for the efficient and confident checking and verification of outputs. Journalists will need
well-designed editing interfaces in order to effectively use generative AI for various tasks.
Respondents are also open to getting help from generative AI for tasks related to
analyzing, getting, or processing data and information, which are perhaps not
coincidentally also the kinds of work activities that respondents rated as boring,
repetitive, or tedious.
• Ethical Concerns and Responsibility. Ethical considerations are paramount, with
concerns about human oversight, accuracy, and bias most prominent. The industry
is grappling with how to balance the benefits of generative AI with the need for ethical
journalism practices, including the banning or limiting of use for particular use cases such
as for the generation of entire pieces of published content. Overall, editors, managers,
and executives (rather than technologists) were the roles that respondents thought
should be more responsible for ensuring effective and ethical uses of generative AI.
• Strategies for Responsible Use. While many organizations are developing or following
guidelines for the ethical use of generative AI, there is a call for clearer, more concrete
guidelines, training, and enforcement to navigate the ethical landscape effectively.
On top of guidelines, there is recognition that additional training is needed to support
responsible use. Other strategies that might also improve responsible use of
generative AI, like more robust procurement of tools that include AI and automation as
well as internal testing and auditing, are rarely mentioned.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 20248
• Ambivalence in Content Rights. Respondents expressed a degree of uncertainty about
whether tech companies should be allowed to train models on news organizations’
content, with some emphasizing the negative commercial impacts and others advocating
to advance the accuracy and reliability of models which could benefit society.
In the next section we examine what the future of newswork could look like in the era
of generative AI. Then we turn to the ethical considerations and approaches needed if
generative AI is going to be incorporated into responsible journalism practice. We finish the
report with a conclusions section where we argue that the industry will require investments
in policy, practices, research, design, and training to further advance and best capture
the value of this technology while aligning it with journalism’s norms and practices of
responsibility to society.

The Future
of Newswork

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202410
Automation often inspires anxiety about how new technologies
like AI might threaten the status quo of work and undermine a
person’s livelihood. If generative AI systems can do basic news
gathering and writing, could they replace reporters and editors?
Or will these tools be more complementary and help to augment
the work? How is all of this going to change the jobs people in the
news industry are asked to do, particularly as user experiences
and expectations also evolve?
Survey respondents are keen to explore a wide range of tasks
to augment their workflow and increase their efficiency, but
there’s also wide variance with more research needed to establish
any actual productivity gains. However, it’s already clear that
generative AI is changing the structure and organization of
work and putting pressure on individuals to learn new skills to
keep up, while also creating new roles and opportunities within
organizations.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202411
Current Usage
If respondents indicated that they or their organization had used generative AI in some
capacity, they were then asked about what tasks they or their organization had used it for.
Responses are coded into broader categories of tasks shown in Figure 2.
The dominant category of use is unsurprisingly related to content production. This category
included responses about using generative AI tools in processes of producing public-facing
or newsroom internal content, including creating, editing, and transforming media formats.
Specifically, the responses were further divided into six different categories: text (69.6%,
126 of 181 responses), multimedia (20.4%), translation (8.8%), transcription (7.2%), user
experience (2.8%), and metadata (0.6%).
In the text category, respondents state they have used generative AI for generating content
such as news headlines, social media posts, newsletters, quizzes, text from data, taglines,
and story drafts. As one respondent noted, “We use AI to help us create outlines, briefs, and
first drafts.” They have also used generative AI for copy editing and summarizing articles,
rewriting for a different medium (e.g. script production) or to reduce jargon or produce
a press release, and fact checking. Respondents also mentioned using generative AI for
generating multimedia content, such as illustrations (e.g. for social media posts), videos,
audio (e.g. text to speech), or for editing images. AI-assisted translation and transcription
also came up as part of the content production process, using tools such as Otter or Whisper.
A handful of respondents mentioned using generative AI to support the user experience to
create consumer-facing chatbots and for assistance with creating metadata such as the
creation of alt-text for images or metadata for audio files.
Another somewhat common usage of generative AI is for information gathering and
sensemaking (21.5%). This category encompasses ways AI is used for news discovery,
research, ideation / brainstorming, and curation, or as one respondent put it, “automation
of research steps, newsgathering, and notification systems.” We also identified some more
technical tasks supported by generative AI: coding (5.0%) encompasses responses that use
generative AI for software development tasks such as code review or “writing and refining
HTML code,” and working with data (7.7%) spans tasks that involve manipulation and
analysis of data or its extraction from documents such as “manipulating spreadsheets” or
“small data cleanup tasks.” Lastly, business (16.6%) is a category used to capture responses
that mention using generative AI for internal business operation purposes, like creating
presentations, drafting emails (e.g. “to sales prospects”), outputting ads or marketing
content, or outputting material for search engine optimization.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202412
Figure 3.
The distribution of
tasks mentioned
in response to the
question “What tasks
have you or your
organization used
generative AI for on
an experimental or
regular basis?” (N=181)
Content Production: User Experience
69.6%
21.5%
20.4%
16.6%
8.8%
7.7%
7.2%
5.0%
2.8%
0.6%
Content Production: Text
Information Gathering & Sensemaking
Content Production: Multimedia
Business
Content Production: Translation
Working with Data
Content Production: Transcription
Coding
Content Production: Metadata
Current Usage Tasks

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Aspirational Usage
Next, to understand better how some journalists want to use generative AI, respondents
were asked to “List at least three tasks that you would ideally like to use generative AI for
in your work, if it were capable of producing quality results.” Results are shown in Figure 4
below.
Beyond the tasks already shown in Figure 3, respondents also listed planning (3.9%),
distribution analytics (2.6%), personalization (1.7%), layout (1.3%), and fake news detection
(0.9%) as ways they would like to use generative AI. In planning, responses mainly request
the use of generative AI to improve daily workflow and news cycle plans. For personalization,
responses point to content customized, suggested, or curated based on reader/user
information (e.g. “personalization of newsletters and homepage”). Fake news detection
reflects a need for identifying and debunking untruthful news content. Layout points to the
need of respondents to have generative AI help with paper news layout during production.
Distribution analytics differs from the need of analyzing data for production processes and
captures the need of collecting and analyzing user engagement data (e.g. “answer questions
about website analytics”). In comparison to current usage, we observe that some aspirational
usage categories gain in interest such as user experiences, working with data, information
gathering and sensemaking, metadata, and business use-cases. The largest absolute gain
in interest was for information gathering and sensemaking, which includes a variety of
reporting-relevant activities such as news discovery, research, ideation, and curation.
For a few of the categories in Figure 4, we also observe new tasks. In responses that were
identified as text content production, respondents mention the need for having generative
AI help with the production of event calendars, which can be a highly structured form of text
production and lends itself to automation. For information gathering and sensemaking,
we observe more needs for assistance with monitoring and scanning different media (social
media, news media, and local government) and alerting when newsworthy information is
identified (e.g. “news-scanning in the local market” and “identifying possible sources”). A
few respondents mentioned the need for AI to help with news aggregation and curation.
For multimedia content production, there are increased needs for AI assistance with video
and audio modes of production (e.g. “short video news explainers”). Additionally, there were
more frequent responses mentioning the creation of AI chatbots for user experiences (e.g.
“Chatbot to act as an interface to all of our content”).

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202414
Figure 4.
The distribution of
tasks mentioned
in response to the
question “List at least
three tasks that you
would ideally like to
use generative AI for
in your work, if it were
capable of producing
quality results.”
(N=229)
78.6%
34.9%
27.5%
23.1%
14.4%
10.5%
8.7%
6.6%
5.7%
3.9%
3.5%
2.6%
1.7%
1.3%
0.9%
Content Production: Text
Information Gathering & Sensemaking
Business
Content Production: Multimedia
Working with Data
Content Production: Transcription
Content Production: Translation
Content Production: User Experience
Coding
Planning
Content Production: Metadata
Distribution Analytics
Personalization
Content Production: Layout
Fake News Detection
Aspirational Usage Tasks
Respondents were also asked directly about the opportunities they perceived for the use
of generative AI in journalism and these responses largely concurred with what they talked
about in aspirational tasks. There was considerable interest in using generative AI to support
data analysis and research, to reduce repetitive tasks and save time, allow for efficient
editing, and also to enable creativity through brainstorming and to explore new possibilities
in personalization. Responses among editors, technologists and executives were similar.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202415
What’s Working and Not Working
Respondents talk about saving time and enhancing efficiency, but also augmenting and
supporting creativity with story discovery, idea generation or brainstorming, and mentioning
specific activities where AI might offer some gains such as content production with
headlines and illustrations, for research in gathering background content, data work for
scraping and extraction from documents, and expanding audiences.
When respondents found AI to be ineffective, they often refer to quality issues relating
to accuracy, trustworthiness, and content quality such as the relevance of headlines
generated or the blandness of the text that commercial large language models produce.
They also point out that sometimes use of the models costs more time than they save or
creates more work like editing, can output biased text, and that also there are issues with
prompting and controlling the models effectively. Some found that it simply took too much
time to edit and “craft prompts that are responsive to needs” to yield much efficiency gain.
New Work, New Roles
Respondents mention new roles being established as a result of generative AI. These include
leadership roles like “innovation officer,” “AI Expert,” “Head of AI,” product positions like
“AI Product Owner,” editorial jobs like “prompt designer/editor/specialist,” “fact checker,”
“AI Video editor,” legal roles, and engineering positions including “Software Engineer,” “AI
+ Automation Engineer” and “quality assurance.” Roles were mentioned at different levels,
from managers to individual contributors and even an internship.
Some of the new positions involve watching for new innovations and keeping up with the
pace of change with a role described as: “a person who monitors turbulent developments
in the field of AI.” New editorial roles include fact checking, prompting, and technical roles
like engineering and user-interface and -experience design. Overall, there is a shift in human
work toward management, product, some new positions, and a lot of technical work to
incorporate and maintain systems that include AI and automation.
Changing Work
Almost half of respondents (49%, 92 of 181 respondents) indicated that tasks or workflows
have already changed because of generative AI. Among the respondents who said tasks and
workflows have changed, they indicate that generative AI models have shaped the structure
and organization of work. In some cases, the structural and organizational changes to their
work are already being reflected in Content Management Systems (CMSs), Slack channels, or
via commonly used office software.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202416
AI models take on roles as collaborators and are used as a sounding board “to bounce
off ideas” or as an editor that catches “things that may have been missed.” AI can shape
relationships between people as well, with one respondent noting: “instead of asking a
colleague for help with a heading, I always ask ChatGPT first.” The idea of AI as a collaborator
also means that people need to think about how to formulate a task for delegation which
might involve determining “if there is a prompt that will increase my efficiency and
productivity.”
Much like for a self-service checkout system in a supermarket, respondents indicate that
new work is created for them when they use these systems, primarily in terms of having
to edit or proofread the outputs of the AI to ensure it is acceptable. In at least one case,
a respondent indicated that this self-service mentality also shaped the relationship with
freelancers: “We [have] stop[ped] hiring freelancers for certain tasks, like basic translations
or copywriting.” As generative AI is increasingly used outside of the newsroom, it can also
create new editorial work to evaluate sources of information, as one respondent described:
“We had to define protocols to detect AI-generated content. We had to put guardrails in
place because we receive a lot of text from external authors… .”
Efficiency Hopes and Realizations
Tasks where efficiency gains from using generative AI seemed to be supported include image
editing, monitoring sources, producing alt-text, SEO text generation, press releases, emails,
social media posts, and reducing time to produce drafts of text. As one respondent put it:
“All the genAI tools in Photoshop save our graphics team hours of time each day.” In terms
of creativity support, another respondent wrote: “being able to quickly see numerous visual
iterations of a concept makes it easy to explore options and ideas I’d otherwise not pursue
due to time or resource restrictions.”
Despite the creation of more editing, prompting, or evaluative work in some cases, many
respondents continue to apply an efficiency frame to how they talk about task and workflow
changes: “We can scale some tasks (such as finding topic ideas) much faster.” Hopes for and
actual evidence of efficiency gains often goes hand-in-hand with talking about how the time
saved by using generative AI can be reinvested into other activities: “The graphics team is
able to redirect resources into working on other aspects of their job with the time saved by
using genAI in Photoshop.” Efficiency also shapes the pace and iterative nature of the work:
“because it’s so fast to adjust a prompt and generate more iterations … [AI] has provided
more opportunities to dabble in different styles and methods.”

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202417
9 We base these on the occupational information available from O*Net online: https://www.onetonline.org
10 See Appendix B Q13 for definitions of these work activities.
Delegating Work
Respondents were asked about how they would evaluate the work they got back if they
delegated a chosen task to a colleague. We asked this to better understand the criteria for
success for the chosen task and with the idea that knowing these criteria could enable ways
to better evaluate task performance if delegated to an AI system.
There was wide variance across different tasks among the responses. A story discovery
task might rely on criteria of “originality” and “relevance” whereas for a content generation
task “clarity” and “concision” might be important. Respondents at times referred to both
subjective personal and formal organizational (including legal) rubrics for help in evaluating
tasks. A few respondents mentioned criteria related to efficiency, productivity (e.g. volume
of output) or general utility, but many more discussed criteria related to content and
information quality, various key news values, audience-oriented factors and whether the
output was checkable or verifiable.
Content and information quality includes many factors that might be applicable in
different contexts including clarity, concision, specificity, timeliness, readability, context,
completeness/thoroughness, publishability, or even just common sense. Also often
mentioned were news values of accuracy, validity (e.g. aware of limitations), relevance, and
originality (e.g. including something exclusive or surprising). Respondents also looked to their
audience to define what it means to have done a task at an acceptable level, mentioning
factors like audience engagement and user feedback. Finally, respondents talked about
whether the output from AI was verifiable or could be checked, which included aspects of
replicability, provenance, explanation, and ease of fact checking.
AI Help
Respondents were also asked if they would want help from AI in their chosen task, which
was then categorized according to a set of generic occupational activities that are relevant
to journalism9. In Figure 5 it’s clear that there is strong interest in delegating activities
related to analyzing data or information (16 “yes,” 4 “maybe”), and some interest for
getting information (11 “yes,” 6 “maybe,” and only 3 “no”), processing information (9 “yes,”
5 “maybe,” 1 “no”), and communicating with people outside the organization (4 “yes” and
3 “maybe”)10. But there’s more resistance and uncertainty around getting help from AI for
activities such as thinking creatively (9 “yes,” 11 “maybe,” 8 “no”), making decisions and
solving problems (6 “yes,” 6 “maybe,” 6 “no”), or internal communicating with supervisors,
peers, or subordinates (2 “yes,” 4 “maybe,” 3 “no”).

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202418
Figure 5.
The distribution
over categories of
responses “No,”
“Maybe” and “Yes”
in response to the
question “Would you
be interested in having
AI help with this task?”
No
4 16
3 4
34 2
1 4 6
3 6 11
3 7
32 7
51 9
118 9
2 2 4
6
666
Maybe Yes
Analyzing Data or Information
Communicating with People
Outside the Organization
Communicating with Supervisors,
Peers, or Subordinates
Documenting/Recording
Information
Getting Information
Interpreting the Meaning of
Information for Others
Making Decisions and Solving Problems
Organizing, Planning,
and Prioritizing Work
Processing Information
Thinking Creatively
Working with Computers
Percent of Responses
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Interest in Receiving Help for Journalism Activities
For respondents who indicated that they would want AI to help with their identified task
(50%, 93 of 186) they mentioned several specific aspects of the tasks. Some indicated that
they would want automation to help with efficiency, reduce repetition, or increase precision,
augmentation to help them do their job better, or to transform the task to a review task
so that they could more quickly check and complete it. These suggestions are informative
because they help frame how people want to leverage AI along various levels of automation,
with a few calling for actual automation but with more looking for augmentation or task
transformation to keep the human in control.
Among respondents who indicated that they might want AI to help with their identified task
(32.2%, 60 of 186), they expressed uncertainty about a range of factors including around
the capability and accuracy of the models, and whether their use of generative AI would
make them overly reliant on the technology. Respondents raised issues of trustworthiness,
humanness, and whether there was enough of a payoff for using generative AI. In addition,

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202419
Figure 6.
Average ratings
for selected task
in response to the
question “To what
extent do you find this
task (or parts of it)
boring, repetitive, or
tedious?” aggregated
according to work
activities. (N=186)
1.6
1.7
1.7
2.0
2.5
2.6
2.7
2.8
2.9
3.0
3.1
Thinking Creatively (n=28)
Interpreting the Meaning of Information for Others (n=16)
Making Decisions and Solving Problems (n=18)
Communicating with Supervisors, Peers, or Subordinates (n=9)
Getting Information (n=20)
Working with Computers (n=8)
Organizing, Planning, and Prioritizing Work (n=12)
Documenting/Recording Information (n=11)
Analyzing Data or Information (n=20)
Communicating with People Outside the Organization (n=7)
Processing Information (n=15)
Tediousness of Journalism Activities
respondents raised issues related to intellectual property and ethics such as confidentiality.
For respondents who indicated that they would not want AI to help with their identified task
(17.7%, 33 of 186), they focused on shortcomings of the technology including of knowledge,
capability, performance, accountability, trustworthiness, or humanness (e.g. requiring
human judgment or human-to-human contact or relationship) and not knowing if there was
sufficient payoff for the investment needed.
Another way to think about whether people might want help from AI is to look at how
tedious, boring or repetitive they find a task. For their chosen task we asked respondents
to rate “To what extent do you find this task (or parts of it) boring, repetitive, or tedious?”
on a scale from 1 (“not at all”) to 4 (“a lot”). The results tabulated by aggregate work activity
are shown in Figure 6. We find that respondents find tasks such as processing information,
communicating with people outside the organization, and analyzing data or information
had a high average rating, whereas activities like thinking creatively, interpreting the
meaning of information, and making decisions and solving problems were rated as much
less tedious. In terms of specific task categories we found that transcription and metadata
production as well as distribution analytics and working with data were rated toward the
higher end of the tediousness scale. These findings reinforce the findings in Figure 5 about
what work activities might benefit workers if AI could help, both in terms of their own
satisfaction as well as alleviating a sense of tedium or boredom induced by the activity.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202420
Key Learnings and Opportunities
News organizations are already extensively exploring the use of generative AI for content
production, from text and multimedia to transcription and translation. Aspirational use
reflects interests in exploring more utility for information gathering and sensemaking,
working with data, business uses, and metadata creation. And while both current and
aspirational uses reflect an emphasis on enhancing existing workflows, there is some
indication of increased interest in creating new user experiences through chatbots and
personalization. At the same time, not all aspirational use cases are well-suited to the
technology, reflecting some potential misunderstandings about what the technology can
do. For instance, tasks like layout of content or the analysis of user analytics are likely best
served by non-generative AI systems such as optimization algorithms or rule-based natural
language generation systems. In assessing the work activities where respondents would like
AI to help, it’s clear that getting information, processing information, and analyzing data or
information are key areas where there is demand and a recognition of moderate-to-high
levels of tedium, indicating potential opportunities to develop better prompts, interfaces,
and workflows.
However, even where there is considerable interest and activity around content production,
questions linger about just how much productivity generative AI can yield. Workflows and
roles are already changing, and in many cases new work is created in prompting models
effectively and in editing outputs. It seems that some tasks may benefit overall, but others
may not. Additional research might study specific tasks over time to evaluate performance
and delve into how new roles are evolving and relate to each other in the overall
organization. Another opportunity is to invest more in training journalists how to effectively
control models through prompting. As one respondent noted: “I get way better results, if I
invest more time and thinking in writing longer and more elaborate prompts.”
Our findings also suggest there is an unmet opportunity to design new interfaces to support
journalistic work with generative AI. In articulating the criteria used to assess work quality
when delegating tasks, respondents indicated that information quality and verifiability were
key dimensions. Journalists still see themselves as editors and checkers of the outputs of
generative AI, which suggests opportunities to create tools and interfaces that encourage
those checks and enable efficient editing and verification. This might, for instance, include
details to support replicability of an analysis, provenance for a number, fact, or source, or a
general explanation that could be used to help assess an output. Perhaps what journalists
need in order to effectively use generative AI are well-designed editing interfaces to
support human oversight.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202421
Looking at respondents’ hesitations toward getting help with a task from generative AI,
it’s helpful to see that there may be some dimensions, such as capability, performance,
efficiency gain or trustworthiness that might be addressed through technical testing and
evaluation, or additional interface design work. In addition, articulating the criteria for
success for a task, whether delegated to a human colleague or an AI system, can inform how
to evaluate whether a task is performed at a high-enough level of quality. Considering both
the hesitations for help and criteria for successful delegation on a task-by-task basis could
inform engineering design approaches that overcome issues and benchmark acceptable
performance levels. For example, in use cases where there is a concern for confidentiality,
running models on local or organizationally owned infrastructure could alleviate that
hesitation. At the same time, some dimensions, such as issues of model accountability or
“humanness” are intrinsic and immutable limitations of the technology.

Ethical
Considerations

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202423
Novel technologies have often influenced journalism as a process
and as a product. Generative AI is no exception, presenting
journalists and media professionals with challenges and ethical
considerations. These include but are not limited to challenges
around source material, intellectual property concerns, and
the bias that is ingrained in these technologies. In the following
subsections we explore challenges and concerns focusing on the
ethical dimensions of developing responsible practices.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202424
Figure 7.
Responses reflecting
concerns around the
use of generative AI.
(N=174)
21.8%
16.4%
9.5%
7.7%
6.8%
6.8%
3.2%
3.2%
2.7%
2.7%
1.8%
No Human Supervision
Inaccurate Info
Bias
Reducing Quality
Job Displacement
Lack of Transparency
Accountability
Plagiarism
Copyright Issues
Originality
Privacy and Data Protection
Concerns
Concerns and Challenges for Ethical Use
We asked respondents if they had ethical concerns about the use of generative AI in
journalism. The most prominent concerns were lack of human supervision (21.8%, 48 of
174 ), inaccuracy (16.4%, 36 of 174), and bias (9.5%, 21 of 174). See Figure 7. Lack of human
supervision feeds into the belief that generative AI would be used without human oversight.
Respondents mention that they are not that concerned as long as the output is reviewed by
an editor, while one noted that “I think reporters must independently verify information or
pay the consequences.” There were also concerns about inaccurate information, including
mis- and disinformation, as generative AI might produce a lot of incorrect output: “I have
large, gaping voids of concern about AI in journalism. Incorrect information, fake images,
bad stories, terrible grammar, job losses, all of it.” Bias is also a prominent concern, as
respondents state that they are aware that the input and the output of generative AI might
contain (hidden) bias. “Hidden biases and inaccuracy are the primary concern. Writing
articles should be kept to humans, and gathering materials should also be done by humans,
even though it’d be much more difficult to verify” noted one respondent.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202425
Other concerns highlight the potential risks associated with using generative AI to produce
journalistic content, particularly the erosion and reduced quality of news (7.7%, 17 of
174). Respondents worry that too much AI content will devalue journalism at a time when
monetizing content directly through reader revenue is proving increasingly crucial. The lack
of transparency of the use of generative AI is mentioned (6.8%, 12 of 174), as respondents
fear that people in the news industry will not disclose whether they have used models like
ChatGPT or Bard (now Gemini). Still other concerns include the threat of job displacement
(6.8%, 12 of 174), the risk of plagiarism (3.2%, 7 of 174), and the lack of originality (2.7%, 6 of
174). Less mentioned concerns were copyright issues (2.7%, 6 of 174) and privacy and data
protection (1.8%, 4 of 174).
Respondents were also asked to formulate some challenges they experience in addressing
ethical concerns. One of the main challenges, respondents mention, is the lack of training
(18.2%, 36 of 196). Training needs include teaching staff about the best practices and
risks of generative AI. Other respondents state that smaller organizations might not have
sufficient resources to invest in training: “ Training is lovely, but time spent on training is time
not spent on journalism – and a small organization can’t afford to do that.” In other words,
there is not only a feeling that respondents are insufficiently prepared for the generative AI
transformation, but also and maybe even more worrisome that there is simply insufficient
time for an investment in training.
Another challenge concerns not having regulation and guidelines in place (11.1%, 22 of
196) or as one respondent put it: “We should have basic guidelines on what kind of things
we check when taking on a tool.” The last prominent concern deals with the lack of quality
control (8.1%, 16 of 196), as respondents worry that outputs from generative AI will not be
verified sufficiently. A respondent states: “I worry that we do not hav[e] ‘standards staff’
in place to fact check AI. News organizations could be viewed as more trustworthy if we
can show that real people enforce the news standards.” A potential gateway into dealing
responsibly with these concerns and challenges, respondents mention, is by deciding what
uses should be banned (15.5%, 34 of 196), which we elaborate further in the next section.
Banned Uses
We asked respondents if there were any uses of generative AI that should be discouraged
in journalism. Among respondents that mentioned bans as part of the response to ethical
concerns, a majority agreed that the generation of entire pieces of content by generative
AI should be banned, as models are not yet reliable for this task (55.8%, 19 of 34). One
respondent stated: “Any generative AI used to create content is concerning. We view
generative AI like a police scanner. We use them to gather information, but still confirm
and decide to report on our own.” The specific bans are also rooted in the general belief
that journalism requires skills that cannot adequately be performed by a machine, and that
outputs of generative AI could contain hallucinations. Other potential uses where bans were
suggested include the generation of interview questions (17.6%, 6 of 34) and replicating

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202426
artists’ styles using generative AI due to concerns regarding accuracy and authenticity
(17.6%, 6 of 34). A respondent wonders: “Interviews often switch gears midway because
of a reporter’s instinct. How will AI match that?” Several respondents proposed to ban the
use of generative AI to create content to mislead or deceive, as doing so would conflict
with journalism’s commitment to trust and integrity in journalistic practices (8.8%, 3 of
34). A respondent states: “We should not generate text, images, or any other reader-facing
information that violates the trust they put in our editors and reporters.” Additionally,
some respondents suggested not to use generative AI for local news coverage and
investigative reporting, underscoring the recognition that AI does not possess the nuanced
understanding or ethical judgment required for these journalistic endeavors.
These suggested bans point to an emerging belief that there are some forms of using
generative AI in journalism that are simply unacceptable. In other words, apart from
concerns about actual productivity gains, ethical considerations and public expectations
toward the role of journalism can be another important reason to refrain from using
generative AI for certain tasks. Of course, this survey is only a snapshot, and it may be
worth revisiting the topic further, once generative AI practices have been more firmly
integrated into journalistic routines and roles. The responses highlight a collective effort
within newsrooms to uphold journalistic standards, safeguard against misinformation,
and prioritize the role of human judgment and ethical considerations in news production.
Responses underscore that having guidelines in place could contribute to upholding these
journalistic standards.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202427
Figure 8.
Responses on various
strategies for ethical
use of generative AI
(N=145)
Strategies for Ethical Use
20.0%
15.2%
14.5%
10.3%
8.3%
8.3%
8.3%
4.8%
3.5%
3.5%
2.1%
1.4%
Not Using It
Verifying the Output
Guidelines and Legal Frameworks
Oversight
Adhering to Journalistic Values
Limit Use
Personal Ethics & Gut Feeling
Dedicated Support Structures
Testing the System
Learning
Peer Exchange
Responsible Procurement
Strategies for Ethical Use
As shown in Figure 8, the most frequently mentioned strategy for use in overcoming ethical
concerns and challenges was not using generative AI (20.0%, 29 of 145). In other words,
1 out of 5 respondents stated that a strategy to ethically use generative AI is to avoid its
use altogether. This also means that ethical concerns can be an important obstacle to
the deployment of generative AI in newsrooms. One respondent mentions: “I think the
use of generative AI in my work is unethical, full stop.” Another strategy that plays a role is
adhering to existing guidelines and legal frameworks (14.5%, 21 of 145). As one respondent
states: “We apply the same standards to AI-generated content/information that we would
to anything else that we publish or rely on. We have to be able to understand it and stand by
our decision to use it.” The strategy of consulting guidelines is closely followed by relying on
personal moral compasses and gut feeling (8.3%, 12 of 145).

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202428
11 https://blog.ap.org/standards-around-generative-ai
12 See: AI Act, Shaping Europe’s digital future.
https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
13 See: A pro-innovation approach to AI regulation.
https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper
Apart from a total ban of using generative AI, respondents emphasize that they should
limit its use (8.3%, 12 of 145) as well as verify the output (15.2%, 22 of 145). Respondents
underscore that they only use it on a test basis and “compare with other known materials
to gauge whether it is accurate.” Additionally, respondents emphasize the need for human
oversight and thorough fact-checking. As one respondent put it: “We strongly rely on our
editorial core values such as facticity, transparency, impartiality, and accountability. These
values have been the foundation of our journalism for almost 80 years. They are ideally
suited to creating an ethical framework.”
Some organizations are awaiting further advancements in generative AI that address
copyright and intellectual property concerns before considering implementation. There is
a mix of readiness, caution, and proactive measures being taken to navigate the challenges
associated with generative AI in newsroom settings. At the same time, responses show
that not all news organizations have strategies in place to overcome these ethical concerns
and challenges. Guidelines play a role, but also an internal gut feeling and moral compass.
Other strategies that might improve responsible use of generative AI, like responsible
procurement of tools that include AI and automation and internal testing, auditing, and
verifying the input are rarely mentioned.
Guidelines
Most respondents (61.2%, 104 of 170 respondents) are aware of various guidelines
surrounding the use of generative AI in journalism, though specific knowledge and adoption
vary among organizations. Some respondents express familiarity with guidelines from news
outlets like The Guardian, NPR, BBC and AP11, and regulatory frameworks like the EU AI
Act12 or the UK’s AI white paper13. Others mention that their organization has its own set of
guidelines (42.3%, 72 of 170 respondents). Common themes in existing guidelines echo the
ethical concerns and challenges including transparency, human oversight, and avoiding and
banning the use of generative AI for producing content entirely.
There are variations in approaches to crafting guidelines; some organizations adopt a more
bottom-up approach by forming working groups to establish guidelines for generative AI,
while others have a more top-down approach by relying on existing industry standards or
guidelines. Additionally, a few respondents indicated that there should be industry-wide
standards in place for the use of (generative) AI, either in combination with self-regulation
or in the form of guideline documents.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202429
Results from the survey emphasize that guidelines should be regularly reviewed,
and adaptable to the latest developments of generative AI. Respondents find many
guidelines high level. There is a need for concretization and operationalization to make
them meaningful for practitioners. Responses include requests for clear delineations
of use cases or specific generative AI tools that should be allowed or banned. Some
respondents emphasize the importance of mentioning which uses of generative AI should
be disclosed to the audience, as well as addressing the specific biases in AI-generated
content. Enforcement challenges and the need for a balance between experimentation
and regulation are also noted as essential by respondents. As one respondent put it: “As AI
evolves, it is not a black and white issue. There has to be room for testing, experimenting.”
Another respondent mentions: “It’s more like a judgment call than a clear set of rules. Also,
we can only enforce them to an extent. How do I know for sure whether specific paragraphs
were AI generated?”
Responses mention that a potential solution for adding more specificity in the guidelines
could be to include an external and internal version of the guidelines. Internal guideline
documents tend to be more detailed, providing information about banned processes, and
what specific software applications to use. The external guidelines are often presented at a
higher level, focusing on broader principles and are more targeted toward transparency with
the audience. Among the organizations that indicated they had their own guidelines, 22.8%
(16 of 70 respondents) noted that they have a separate internal version. Having guidelines
in place is only one requirement for potential responsible use of generative AI, or as a
respondent states: “I think we are paving the road as we are driving – it is a new technology
that seems to explode out of a box and now we are trying to navigate a world where new
‘amazing’ AI tools are dropping left right and center.”
Who Is Responsible?
While a focus on guidelines would seem to assign responsibility to the users of generative AI
for ensuring responsible use, we also asked respondents to rank various other stakeholders
who might be tasked with ensuring the responsible use of generative AI in journalism,
including reporters, editors / managers, technology vendors, executives, staff technologists,
the legal department, and unions. Each respondent ranked each of those stakeholder
roles from 1 to 8, where 1 presents greater responsibility for ensuring the responsible use
of generative AI in journalism (See Figure 9). Overall, respondents report that editors and
managers should have the greatest responsibility for ensuring the ethical use of AI (average
rank: 2.5), followed by executives (average rank: 3.3) and reporters (average rank: 3.8). At the
bottom of the ranking, respondents tend to put unions (average rank: 6.2) and technology
vendors (average rank: 5.6) as having less responsibility for ensuring the responsible
use of AI.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202430
Figure 9.
Average ranking across
8 stakeholder roles
when asked “Who
do you think bears
greater responsibility
for ensuring the
responsible use
of generative AI in
journalism?” Lower
numbers indicate
a higher ranking of
responsibility.
Average Rank for Who Should Ensure the Responsible Use of Generative AI
3.3
3.8
4.6 4.6
5.4 5.6
6.2
2.5
Editors/
Managers
Executives Reporters Staff
Techno-
logists
Legal
Department
Legislators Technology
Vendors
Unions
News Content as Training Data
Respondents were asked if they thought other companies should be allowed to train
their AI models on news organizations’ digital reporting and information. The biggest
group of respondents seems torn, as they responded “maybe” to this question (53.6%,
87 of 166). One respondent states: “In theory this sounds like a good idea but it’s scary
to think of not having control over how content is used. (...). Is it fair to let tech giants
profit on the shoulders of the reporters grinding out the hard work?” While recognizing
the potential for revenue generation and advancements in AI tools, these respondents
emphasize the need for careful consideration of copyright issues, transparency, and
accountability to protect intellectual property and journalistic integrity. Additionally,
there is a need for transparency and accountability in how the data is used and whether
proper attribution is given to the original creators. Another respondent mentions: “Good
inputs means good outputs. News is verified and high standard content. The ‘maybe’ is
about proper compensation and tech companies taking their safety remit seriously.”
Those opposing the idea of allowing other companies to train models on their digitized
information (32.5%, 54 of 166) express concerns about copyright infringement,
unauthorized use of proprietary content, and the potential negative impacts on the
competitiveness and sustainability of news organizations. Skepticism surrounds the
fairness of contributing valuable data without compensation or control over its use, with
worries about bias, misinformation, and loss of public trust in journalism. A respondent
states: “Why would you? If a company that wants to make profit needs our content to do
so, they can either pay for it or share the profits. Taking someone’s work and using it for
your own benefit is simple theft.”

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202431
Those advocating for allowing companies to train on their digitized archives (13.9%, 23
of 166 responses) argue that such collaboration could significantly advance the field by
improving AI model accuracy and reliability, benefiting the news industry and society.
Collaboration is seen as vital for producing accurate, fact-checked content while adhering
to professional and ethical standards. Additionally, respondents highlight the potential for
revenue generation and practical benefits for reporters, emphasizing the importance of
transparency and copyright adherence in collaborative efforts. Overall, allowing access to
news data for AI training is viewed by some respondents as a mutually beneficial endeavor
that can enhance the quality of AI-driven journalism while respecting journalistic integrity
and legal considerations.
Key Learnings and Opportunities
Our results reveal that respondents have a range of ethical concerns about the use of
generative AI. About a quarter of our respondents even indicate that these ethical concerns
are a reason for not using generative AI, or only in a limited way. Addressing these ethical
considerations and challenges are vital for the responsible implementation of generative AI.
The most pressing concern is linked to losing control, or having a lack of human oversight.
Other prominent concerns address the quality of the output (accuracy, bias, originality,
transparency). Less prominent concerns include copyright issues or job displacement
which suggest few of our respondents are worried about job losses due to the technology.
Having said so, these results can also have been influenced by the composition of our
respondents and their roles in the organization. Other less prominent concerns were the
lack of transparency or disclosure when generative AI is used, both internally (inside the
news organization) and externally (toward the audience). Among the respondents who
stated that uses of generative AI should be banned, the majority mentioned the generation
of entire pieces of content. Other suggested bans include generating interview questions
and replicating artists’ styles using generative AI due to concerns regarding accuracy and
authenticity.
When asked about overcoming these concerns, 1 in 5 respondents mention that they
require training to use generative AI more responsibly. In other words, there is currently
not only a feeling that respondents are insufficiently prepared for the generative AI
transformation, but also and maybe even more worrisome that there currently is simply
not sufficient room for and investment in training. At least for Europe, in the forthcoming
AI Act there will be a requirement for providers and professional users, such as media
organizations, to take measures to ensure the AI literacy of their staff, taking into account
also the context in which the technology is being used. One in 10 respondents also
emphasized the importance of having guidelines in place.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202432
We observe that the concerns that were mentioned are closely related to the use of
generative AI as a tool. It is also valuable to report issues that we know to be of concern but
which were not mentioned at all by respondents. In the public discourse around generative
AI, concerns about the environment and the ecological footprint of generative AI, extractive
labor practices and the working conditions of AI workers, the growing power imbalance and
dependency of the media on large tech companies, the danger of further reinforcing social
injustice and disparate treatment or even more alarmistic calls about existential threats
to humanity figure prominently. None of these concerns were reflected in the responses,
which remained focused on daily journalistic practices. Considering that journalism does
have an important role in informing the public discourse, there is a need to explore whether
this lack of concern for broader ethical issues is the result of a mental disconnect, lack of
awareness or the way the questions were framed.
Strategies for using generative AI responsibly were focused on monitoring the output and
far less on strategies to monitor the input and the actual models. Some of the prominent
concerns that were mentioned like bias, lack of transparency, and lack of accuracy can
already manifest themselves earlier in the generative AI development process and can
also be addressed (potentially more efficiently) by the model provider. Put differently,
throughout the survey responses, we observed very limited critical engagement with ethical
and legal concerns at the level of the input (training data) and the model, and by extension
the trustworthiness of the technology itself. In part, this could be explained by the fact that
most commercial proprietary large language models are not particularly transparent about
training data and the model, and partly this could also be a consequence of the need for
more training and AI literacy that many survey respondents flagged. It remains to be seen to
what extent forthcoming legal mandates will be more transparent about the way generative
AI models have been trained (for example under the European AI Act) might also result in
actual scrutiny and more critical assessments of the tools used.
Guidelines are an important instrument for using generative AI responsibly, but
respondents emphasize the need for a more dynamic approach. Some results reveal that
the responsible use of generative AI should be seen as a living document rather than a
static set of rules. These guidelines should also be more concrete, with more specific
examples of which tools should and should not be used. However, guidelines that are too
specific could undermine experimentation with generative AI by overspecifying behaviors.
When evaluating the responsible use of generative AI, we have observed that gut feeling
and personal moral compasses play an important role for some respondents, although we
could question if this “subjective” feeling is sufficient for deciding what responsible use of
generative AI is. One of the challenges is how to align, enforce and translate often vague
principles and guidelines into practices on the work floor.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202433
When asked about the use of news content as training data for generative AI, the biggest
group of respondents, namely half of them, are torn. Results reveal that respondents see
potential for revenue generation, but at the same time they emphasize the need for careful
consideration of copyright issues, transparency, and accountability to protect intellectual
property and journalistic integrity. The ones that are in favor of allowing companies to train
on their news reporting, fewer than 14% of respondents, argue that such collaboration
could significantly advance the field by improving AI model accuracy and reliability,
benefiting the news industry and society.
Our results reveal that the meaning of responsible use of generative AI depends on the
outlet involved, and a few respondents mentioned interest in industry-wide guidelines
that could be adapted. Respondents did mention some common guidelines at a more
abstract level that include transparency, human oversight and specific banned uses. In
short, responsible use and implementation of generative AI takes time and resources.
Respondents state that we are in the early stages of finding out what responsibility
in relation to generative AI means. The news industry needs time to learn new skills,
respondents say, and they need to actively experiment in line with already existing guidance
and guidelines.

Looking Ahead

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202435
The news industry has rapidly reacted to the wave of generative AI technology that is
working its way through society. By adapting workflows, adding new roles, and developing
approaches to use generative AI technology in its practices we see signs of the evolution
of newswork and of responsible practice in light of the capabilities and limitations of the
technology. Yet, there are a whole host of areas where additional investment and action is
needed:
• Usage policies such as guidelines could be made more concrete to better steer
practitioners toward responsible use around specific tasks and use cases. And tools
themselves could be evaluated more rigorously and systematically to ensure alignment
with journalistic expectations and norms for accuracy, bias, privacy, and so on so that
use is more responsible by default.
• Guidelines alone are not enough though, and need to be effectively implemented
into working processes and routines to establish practices of responsible use, including
practices of human oversight, responsible experimentation and the creation of
dedicated support and learning structures.
• Additional research is needed to establish an evidence base around which tasks and use
cases actually benefit in terms of efficiency and performance gains, as well as to
elaborate the criteria to evaluate success and quality output for a range of tasks.
• Design and prototyping might be used to explore more powerful interfaces to support
human oversight and editing of generative outputs, while also exploring genuinely new
experiences rather than just the optimization of existing workflows.
• And new training programs are needed, not only in prompt writing but also in
responsible use and adherence to usage guidelines (or other policies) as well as in
thinking systematically about how to evaluate and refine workflows or strategically
about how to develop entirely new ones.
In short, news organizations are still in the early phases of the proliferation of this powerful
new technology, and much work remains to realize its full potential for journalism by
advancing on policy, practices, research, design, and training.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202436
14
15 https://www.aim4dem.nl
Disclosures and Acknowledgements
The Associated Press has licensed select text archive content to OpenAI for training.
OpenAI provides access to select technology as part of the agreement14. Effort for Nicholas
Diakopoulos and Charlotte Li on this report is supported by a grant from the John S. and
James L. Knight Foundation. Hannes Cools and Natali Helberger are members of the AI,
Media & Democracy Lab15, Amsterdam, which supported their efforts for this research.
https://www.ap.org/media-center/press-releases/2023/ap-open-ai-agree-to-share-select-news-content-and-
technology-in-new-collaboration
https://apnews.com/article/openai-chatgpt-associated-press-ap-f86f84c5bcc2f3b98074b38521f5f75a

Appendix

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202438
17.6%
12.4%
17.9%
13.8%
14.8%
11.4%
4.5%
3.8%
2.1%
1.0%
0.7%
0-5
5-10
10-15
15-20
20-25
25-30
30-35
35-40
40-45
45-50
50-55
Figure A1.
Respondents occupy
a variety of roles in
their respective news
organizations. The
majority of the survey
respondents were
either an editor or an
executive. Of people
who chose the “Other”
category, we observe
roles such as visual
journalists, product
managers, multiple
role positions, and
consultants. (N=290)
Respondents by Roles in Their Organization
Appendix A.
Participant Sample
Respondents by Years Worked in the News Industry
Figure A2.
The survey reached
an expansive range of
respondents in terms
of their length of time
worked in the news
industry. Among the
respondents, the most
senior indicated 54
years, and the newest
to the industry has
had less than a year
of experience in the
industry. To present
the data, responses
were binned into
5-year intervals.
(N=290)
34.5%
20.0%
18.3%
17.9%
9.3%
Editor
Executive
Reporter
Other (please specify)
Technologist

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202439
Figure A3.
A majority of survey
respondents conduct
work in the West.
Survey responses
of countries were
aggregated into
continental regions
based on Our World in
Data16 classifications.
(N=290)
Respondents by Geographical Region
Respondents by News Organization Types
Figure A4.
Respondents
represent a diverse
range of news
organizations, ranging
from digital native
media to broadcasters
(public and private).
(N=290)
61.7%
24.8%
7.9%
2.8%
1.7%
1.0%
North America
Europe
Asia
Africa
Oceania
South America
21.0%
17.2%
15.2%
14.5%
12.8%
10.3%
6.6%
2.4%
Digital Native Media
Legacy Newspaper
Other (please specify)
Media Group
News Agency
Public Broadcaster
Commercial Broadcaster
Magazine
16 https://ourworldindata.org/grapher/continents-according-to-our-world-in-data?overlay=data

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202440
Figure A5.
Respondents
represent a variety
of newsroom sizes
in terms of full-time
editorial employees,
with people from
both very large (100+
editorial employees)
and small (1-10
editorial employees)
news organizations.
(N=290)
Respondents by News Organization Size
Respondents by Technical Team Size
Figure A6.
A majority of
respondents report
the technical team
size of their news
organizations are
smaller than 10
employees, with 17.6%
of these respondents
reporting they do not
have a technical team
at their organizations
and 14.5% of
respondents reporting
having technical
teams larger than 100
employees. (N=290)
23.4%
14.1%
14.1%
14.1%
31.7%
2.4%
1-10
10-25
25-50
50-100
100+
Not sure
17.6%
36.2%
7.2%
7.9%
4.8%
14.5%
11.7%
0
1-10
10-25
25-50
50-100
100+
Not sure

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202441
Figure A7.
A majority of
respondents identify
as men. Among
respondents who
self-described their
gender identities,
one described their
identity as non-binary
femme. (N=290)
Respondents by Gender
34.5%
58.3%
1.4%
0.7%
5.2%
Woman
Man
Non-Binary
Prefer to self describe
Prefer not to answer

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202442
Appendix B.
Survey Questions
Q1. Please indicate your current job title/role (space provided)
Q2. How would you classify your role?
• Executive
• Reporter
• Editor
• Technologist
• Other (space provided)
Q3. How many years have you worked in the news industry? (space provided)
Q4. What country do you work in? (space provided)
Q5. What kind of news organization do you work for?
• Digital Native Media
• Legacy Newspaper
• Magazine
• Media Group
• News Agency
• Public broadcaster
• Commercial broadcaster
Q6. What is the size of your news organization in terms of full-time editorial
employees (i.e. reporters, editors, etc.)?
• 1-10
• 10-25
• 25-50
• 50-100
• 100+
• Not sure
Q7. What is the size of your news organization in terms of full-time technical
employees (i.e. data science, software developer, etc.)?
• 0
• 1-10
• 10-25
• 25-50
• 50-100
• 100+
• Not sure

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202443
Q8. Indicate your level of agreement with the following statement:
“I am knowledgeable about generative AI.”
• Strongly disagree
• Disagree
• Neither agree nor disagree
• Agree
• Strongly agree
Q9. We are interested in issues of gender diversity relating to AI.
Please indicate your gender: How do you identify?
• Man
• Woman
• Non-Binary
• Prefer not to answer
• Prefer to self describe (space provided)
Q10. Have you or your organization used generative AI in some capacity? (Yes/No)
• You responded that you or your organization use generative AI in some capacity.
• Q10a. What tasks have you or your organization used generative AI for on an
experimental or regular basis? (space provided)
• Q10b. Based on the tasks where you or your organization have regularly or
experimentally used generative AI, please explain how it has or hasn’t been
effective in meeting your needs and expectations. (space provided)
• Q10c. Have any of your tasks or workflows changed as a result of generative AI?
(Yes/No)
• Q10d. You responded that tasks or workflows changed as a result of
generative AI. How so? (space provided)
Q11. List at least three tasks that you would ideally like to use generative AI for in your
work, if it were capable of producing quality results. (Five spaces provided)
Q12. Has your organization created any new positions that are specifically geared
towards using generative AI? (Yes/No)
• Q12a. You responded that your organization has created new positions that are
specifically geared towards using generative AI. What new job titles/roles were created

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202444
and what does that person do? (space provided)
Q13. From the following options, please indicate all areas of work activity that you
find important in your daily work:
• Getting Information — Observing, receiving, and otherwise obtaining information from
all relevant sources.
• Communicating with People Outside the Organization — Communicating with
people outside the organization, representing the organization to customers, the public,
government, and other external sources.
• Interpreting the Meaning of Information for Others — Translating or explaining what
information means and how it can be used.
• Identifying Objects, Actions, and Events — Identifying information by categorizing,
estimating, recognizing differences or similarities, and detecting changes in
circumstances or events.
• Communicating with Supervisors, Peers, or Subordinates — Providing information to
supervisors, co-workers, and subordinates in various modalities.’
• Establishing and Maintaining Interpersonal Relationships — Developing and maintaining
constructive and cooperative working relationships with others.
• Performing for or Working Directly with the Public — Performing for people or dealing
directly with the public.
• Updating and Using Relevant Knowledge — Keeping up-to-date technically and applying
new knowledge to your job.
• Thinking Creatively — Developing, designing, or creating new applications, ideas,
relationships, systems, or products, including artistic contributions.
• Documenting/Recording Information — Entering, transcribing, recording, storing, or
maintaining information.
• Organizing, Planning, and Prioritizing Work — Developing specific goals and plans to
prioritize, organize, and accomplish your work.

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202445
• Working with Computers — Using computers and computer systems to program, write
software, set up functions, enter data, or process information.
• Analyzing Data or Information — Identifying the underlying principles, reasons, or facts
of information by breaking down information or data into separate parts.
• Making Decisions and Solving Problems — Analyzing information and evaluating results
to choose the best solution and solve problems.
• Processing Information — Compiling, coding, categorizing, calculating, tabulating,
auditing, or verifying information or data.
• Monitoring Processes, Materials, or Surroundings — Monitoring and reviewing
information from materials, events, or the environment, to detect or assess problems.
• Evaluating Information to Determine Compliance with Standards — Using relevant
information and individual judgment to determine whether events or processes comply
with laws, regulations, or standards.
• Scheduling Work and Activities — Scheduling events, programs, and activities, as well as
the work of others.
• Judging the Qualities of Objects, Services, or People — Assessing the value, importance,
or quality of things or people.
Q14. Now select one broad category of work activity that you indicated was
important, for which you will answer some more specific questions: (select one
category from prior question)
Q15. Within the one broader category of activity you selected, please describe a
related specific task that you do in your work? (space provided)
• Q15a. How often do you do this task in the course of your job?
• Never
• Rarely
• Sometimes
• Often
• All the time
• Q15b. To what extent do you find this task (or parts of it) boring, repetitive, or tedious?
• Not at all
• A little
• Somewhat
• A lot

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202446
• Q15c. If you were to delegate this task to a colleague you managed, and were responsible
for the output, what criteria would you use to evaluate whether the task was done to an
acceptable level of quality? (space provided)
• Q15d. Would you be interested in having AI help with this task? (Yes/Maybe/No)
• You replied ‘yes’ to the question about having AI helping with the task. What
aspects of this task specifically would you want AI to help with? (space provided)
• You replied ‘maybe’ to the question about having AI helping with the task. What are
you unsure about in terms of having AI help with this task? (space provided)
• You replied ‘no’ to the question about having AI helping with the task. Why do you
not want AI to help with this task? (space provided)
Q16. What do you see as the opportunities for the use of generative AI in journalism?
(space provided)
Q17. Do you have ethical concerns about the use of generative AI in journalism? Are
there any specific uses that should be discouraged? (space provided)
Q18. What are the greatest challenges for responsibly using generative AI within your
organization? How might your organization overcome those challenges to help you
use generative AI more ethically? (space provided)
Q19. Are you aware of any guidelines around the use of generative AI in journalism?
(Yes/No)
• Q19a. You answered that you are aware of guidelines around the use of generative AI in
journalism. Which ones are you aware of? (space provided)
Q20. Does your news organization have its own set of guidelines for the use of
generative AI? (Yes/No)
• You responded that your news organization does have its own set of guidelines for the
use of generative AI.
• Q20a. To what extent do you find them helpful in deciding what is ethical use?
• Not at all
• A little
• Somewhat
• To a large extent
• To a great extent
• Q20b. What do you think might be missing? (space provided)
• Q20c. Are the guidelines enforced? (Yes/No)
• Q20d. Are there separate externally and internally facing versions of the guidelines?
(Yes/No)
• You responded that there are separate external and internal versions of
guidelines. Please elaborate any differences between the two. (space
provided)

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202447
Q21. What strategies do you use to decide what is the ethical use of generative AI in
your work? (space provided)
Q22. Who do you think bears greater responsibility for ensuring the responsible use of
generative AI in journalism? [Rank order the following]
• Reporters
• Editors/Managers
• Technology vendors
• Executives
• Staff technologists
• Legal department
• Unions
• Legislators
Q23. Do you think news organizations should allow other companies to train
generative AI models on their published data/content? (Yes/Maybe/No)
• You responded that news organizations should allow other companies to train generative
AI models on their data. Why? (space provided)
• You responded that news organizations should NOT allow other companies to train
generative AI models on their data. Why? (space provided)
• You responded that news organizations should maybe allow other companies to train
generative AI models on their data. Why? (space provided)
Q24. What do you think labor unions should be requesting when it comes to the use of
generative AI in news production? (space provided)

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem | April 202448
Appendix C.
Additional Resources
Below are links that can provide guidance for your use of AI in the newsroom:
Generative AI in the Newsroom, a collaborative effort led by Nick Diakopoulos
to figure out how and when (or when not) to use generative AI in news production.
https://generative-ai-newsroom.com
AI, Media & Democracy Lab, an ethical, legal, and societal laboratory focused on the
implications of AI for media and democracy led by Natali Helberger.
https://www.aim4dem.nl
AI @ AP, the Associated Press’ work on AI including its first report published in 2022,
free online courses and its five AI projects for local newsrooms.
https://ai.ap.org
AI Transparency initiative led by Nordic AI Journalism.
https://www.nordicaijournalism.com/ai-transparency
Council of Europe Guidelines on the responsible implementation of artificial
intelligence systems in journalism.
https://rm.coe.int/cdmsi-2023-014-guidelines-on-the-responsible-implementation-of-
artific/1680adb4c6
Partnership on AI offers a procurement guide on AI tool adoption for newsrooms.
https://partnershiponai.org/ai-for-newsrooms