By Nayda Garcia
@redlineproject
AI disclosure: ChatGPT was was to create the images and the summary
Summary: AI is transforming journalism by increasing efficiency and lowering costs, but it risks undermining its public-service mission. While it can expand access and speed reporting, it may reinforce bias, reduce journalistic skills, and prioritize engagement over truth. Trust, equity, and accountability depend on how AI is governed, used transparently, and balanced with human oversight in news production.
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People always say how they don’t use AI and it’s a common misbelief that AI is something recent. But the reality is that AI has existed since the 1950s and Big Tech firms like Google, Meta and Amazon have used it for years.
AI is not going anywhere — and journalists are finding a new ways to use it.
There are news organizations and outlets using AI now to draft news reports, summarize press releases, analyze data, give ideas for headlines, personalize feeds, and to monitor audience engagement. There are also some new outlets experimenting with AI-generated graphics or automated transcripts.
Some use AI tools to identify trends or research stories at a faster rate. Since there are newsrooms facing declining advertising revenue and staff sizes are shrinking, AI is seen as a necessary thing to use. It’s quick, efficient, and creates lower costs for production.
Journalism’s mission is to be a public service to its community. It is not meant to be a capitalist industry, but it needs to earn money to operate. It was made to help inform communities, give a voice to diverse communities, hold big powerful institutions accountable, and support democratic engagement and participation. It is supposed to serve its community, not make money from its community.
Additionally, journalism helps shape how the public understands politics and societal issues that influence public policy. An example of this influence would be, a communities’ reactions and responses to crises and how voters are making decisions. Since this is the main purpose of journalism, when this starts to shift, it becomes a problem and creates consequences.
The rise and demand of using AI-driven content in journalism is shifting its important mission of public service into something different. The use of AI in journalism is changing the production of knowledge, how equity is either reinforced or not, and how society is participating in the information system of journalism. It’s not that AI is simply taking journalists’ jobs; it’s more than that.
It’s changing how journalism operates as a whole. There are ways in which it could have a positive impact. For example, by increasing efficiency and expanding access. And a negative effect would be that it increases the reproduction of bias, worsens capitalist tactics, and weakens the relationship of trust with the audience if it’s not used with caution. The use of AI though, is not neutral or automatic. It all depends how these AI systems and tools work in journalistic practice.
Journalism plays an important role in the production of knowledge. It’s not used only for news reports, it also chooses, frames, and interprets them for an audience. Editors make a decision on which stories receive attention. Reporters then decide which sources are credible. Headlines are especially important because they influence what emotional responses audiences will have. These choices influence what society understands as important and truthful. In this way, journalism helps create a shared understanding of reality.
Once AI becomes a big part of the reporting and writing process, it helps in shaping that shared understanding. Professor Zach McDowell, who teaches communications, and media theory at the University of Illinois Chicago, said that there is no such thing as a neutral tool. Technology is never just a passive instrument. It changes possibilities, influences decisions, and reshapes outcomes.
When McDowell was asked whether he sees AI tools as a neutral tool, he compared AI to a car to make a point that there is no such thing as a neutral tool.
“Well there’s no such thing as a neutral tool,” he said. “Tools are not good, they’re not evil, but they’re also not neutral. They do things. A car can get you from place to place, but it also can kill people.
“It uses fossil fuels or electricity made by fossil fuels, and it takes up more space than a pedestrian or a bus per person, or a bicycle.”
In a similar way, AI might help journalists in a positive way by allowing them to produce content faster, but it also can shift how knowledge is constructed. If AI is being used to help draft summaries, help create headlines, and organize the information to its liking, then it’s also influencing how stories are framed before they even reach the public audience.
According to a Journalism and Media review artice, AI is taking over what people think is right and turning it into what it thinks is right. These are automated systems that are meant to assist, not change, because when algorithms decide what matters, people are using their interpretation vs their own. This leads us to understand knowledge in the same way AI does, which is through automation.
Also, the reliability of the information has to be considered. AI creates information based on the patterns it has learned from the data it was trained on. It does not have the capacity for understanding the meaning of the information it creates. Therefore, it creates information that, although sounds correct, is actually wrong.
In the field of journalism, the use of AI in the production of information, credibility is a very crucial factor. Even the slightest mistake in credibility has the potential of messing up the credibility of the audience towards you. Therefore, the use of AI in the production of information, if not handled carefully, has the potential of spreading misinformation, which has implications.
McDowell mentioned the skills that go into the field of journalism, the death of the skills, and the skills that go into the field of journalism. For example, the skills include interviewing, checking facts, and critical analysis, among others. These skills require years of practice. McDowell stressed the importance of practice and failure, especially in journalism: “The reason why people are really good at things, is because they’ve spent time being humble.”
For example, if people start using AI for too many of those skills they cannot perfect, then the journalist will start using it more and more, and they will lose those skills. And then it starts becoming a problem because the more they do this, the worse the quality of the information they are sharing with the public. This becomes a problem because the job of a journalist is a service to the public and if the skills that the journalist is using are dying, then the knowledge they produce is not going to be precise.
The information that currently exists is the information that is going to be used for the AI and the information that currently exists is also going to be the information that shows the patterns of the past. For example, if a certain group was not covered in the past, then the AI is still not going to cover them.
This tells us the AI is not going to care if the information was biased or not. It is going to do what the information shows it to do. And then the knowledge that is created is not going to be more inclusive. McDowell shared negative ways in which AI can be trained, like Grock. “Elon Musk was like, oh, we just updated Grok. It’s going to sound a little bit different. And within like two days, it was calling itself MecaHitler. Why? Because Elon Musk said, “The first thing is saying, you have to be anti-woke.”
Equity is a fundamental part of journalism’s public-service mission. Journalism should reflect diverse experiences and provide fair representation. It should amplify marginalized voices and challenge systems of inequality. However, AI-driven content production raises serious concerns about whether equity will improve or worsen.
McDowell said that historical information systems contain systemic bias. Women, people of color, and lower-income communities have often received less coverage or have been portrayed negatively. Because AI systems are trained on older data, it’ll continue to have systemic bias reflected in its work, even if journalists or newsrooms try to improve representation in their reports.
An example of this systemic bias being embedded into these tools is, if reporting on political leaders is focused on males then AI is going to continue linking authority to men. If there were specific demographics that were repeatedly portrayed as criminal or unstable then AI will repeat those patterns. For example, if political reporting historically focused more on male leaders, AI may continue associating authority with men. If certain communities were frequently portrayed as criminal or unstable, AI may repeat those patterns. These biases may not be intentional, but they are embedded in the training data.
The Tow Center for Digital Journalism reported that AI is being used more and more not just for content generation but also to target a specific audience and report strategy. There are AI tools that are creating engagement metrics like clicks, shares, and time spent reading specific pieces. While engagement helps generate revenue, it may conflict with the goals of public service.
Content that is heavily based on getting strong emotions from an audience or how sensational it may be, tends to spread faster than equitable reporting. If AI tools are being designed, just to increase engagement as much as possible, then they might promote chaos and controversy instead of carefully analyzed content meant to inform audiences. Stories that are created simply to generate a strong reaction from the audience might be prioritized over stories that require deeper attention. These changes can harm equity in journalism. Social issues affecting more marginalized communities might only receive traction only when they’re overdramatized instead of when they require to be heard and supported.
In a more positive light, AI could help improve equity. Newsrooms with limited staff can actually use AI tools to analyze public records, identify patterns in the government, and translate stories to other languages. This can expand access and allow journalists to cover issues they previously lacked resources to investigate. In this sense, AI can reduce some barriers.
However, the key issue is governance. Who designs the AI systems? Who decides what data they are trained on? Who sets the rules for how they are used? Professor McDowell mentions that ownership was what people needed to focus on because big tech companies tend to control massive amounts of AI.
If these companies have AI focus on their interests and developments it would allow them to achieve profit goals that weren’t supposed to be there in the first place. Strong ethics are needed, to be the backbone of AI because without it, it would become a fight for needs of the company vs needs for the people that use the technology from that company. Without that, the inequality between those factors will only get worse.
Participation is another way that allows journalism and democratic societies to stay informed through discussions, voting and activism. Those three values are exactly what participation is meant to be. Journalism provides the information necessary for that engagement.
On the other hand, content focused from an AI’s perspective changes participation in a number of ways. The biggest positive could be how much faster newsrooms could publish updates. AI tools are very good at summarizing data in only a couple of minutes and the faster the public knows, the faster citizens could be informed on the new changes in their communities.
AI can also improve accessibility. Automated translation systems can make news available in multiple languages. This could be used through tools like “text to speech” which you can use on your phone for example. The benefit to that is that content on people’s phones is already personalized to their interests which allows these features to be used more often.
While these are all positives to improving participation, but it can’t be only about access because these AI tools aren’t always available and may not help each cause at hand. The reason for this is, if AI were to get involved in a serious debate or act, it could allow the voices with power to have even more with the use of AI. The more dominant the mainstream is, the harder it would be to keep communities with little to no resources to these tools in the light .
McDowell sees this as a pipeline problem. This issue comes from the fact that if fewer people become journalists, the fewer stories that would come out about communities without theresources a typical community would have. AI systems trained on existing content will mirror those imbalances. Participation in knowledge production becomes limited, not expanded.
Trust also plays a critical role in participation. Journalism relies on a relationship between reporters and audiences. This means that if readers start to think a journalist is using AI generated responses, if people reading your content start to suspect there may be use of AI, trust then begins to decline. Professor McDowell makes a good comparison of this to a personal relationship. Once trust is broken in a relationship, it’s difficult to go back to the level of trust you had before.
Transparency about AI use is essential in journalism. If news organizations are using AI, whether it’s to help them write a story, come up with headlines, etc, then being clear about that would be having good transparency with your audience. Which can build a stronger relationship with your audience. They must explain how human journalists verify and edit content. Participation depends on confidence in the integrity of information. If you don’t have that transparency with your audience, then public engagement declines.
AI-driven journalism also changes its public service mission due to economic and environmental societal pressures. Journalism has gone through financial instability for some time now. McDowell said, “People and journalists are not paid enough, the way in which we generate revenue for journalistic things has been quashed for decades and decades already. Journalism isn’t dying but the way in which we can pay for it has been actively being bled out.”
Many local newspapers have also closed, and AI is often presented as a solution to economic decline because it reduces certain labor costs.
However, economic efficiency does not automatically align with public service. If AI is used due to less newsroom staff it may actually end up making it worse and fewer reporters may investigate local issues or attend community meetings. Public accountability may weaken. The goal of lowering costs may conflict with the mission of serving the public.
The environmental impact on journalism is often overlooked. When a large amount of AI systems is used, that requires powerful data centers which consume significant energy and water. These environmental costs are part of the broader consequences of adopting AI technologies. Journalism’s public-service mission includes responsibility to communities, and environmental sustainability is part of that responsibility.
These economic and environmental factors indicate that the transformation of journalism by AI is not limited to the assistance provided in writing content. Public service can also be affected.
AI-driven content is changing knowledge production by automating parts of the reporting and writing process. It has implications for equity in terms of historical biases and interactions with engagement-driven systems and implications for participation in terms of access, representation, and trust.
AI influences outcomes and reflects existing social structures. It has the potential to aid journalism and journalism outlets in its efficiency and struggles. However, it also has significant risks in terms of fairness, accountability, and democratic engagement.
The future of journalism will depend on the responsible use of AI in journalism. If transparency and equity are prioritized and valued in journalism, AI can be a powerful tool to enhance journalism. But if capitalistic values and the use of AI for automation are prioritized and valued in journalism, AI can lead to greater inequality and undermine the democratic role of journalism, taking us backwards.