The AI backlash is real. Newsrooms are caught in the middle.

A New York Times/Siena poll released this week found that 35 percent of Americans think artificial intelligence is “mostly bad.” Only 16 percent think it’s “mostly good.”

Sit with that for a second. The technology that every media company is rushing to implement — the thing your leadership is mandating, your vendors are pitching and your competitors are announcing — is viewed unfavorably by more than a third of the country.

When the Hard Fork hosts put that number to Google CEO Sundar Pichai this week, he didn’t spin it. He said people’s anxiety is legitimate, that humans aren’t built to process change at this pace, and that the industry has more work to do.

He’s right. But he’s also the CEO of a company that sells AI products. His problem is winning back public trust. Your problem, if you run a newsroom, is more complicated than that.

News organizations are caught in a specific bind that Pichai doesn’t have to navigate.

You are simultaneously trying to cover AI fairly — to report on job displacement, on bias, on regulatory battles, on the legitimate concerns that 35 percent of your audience holds — while also implementing the technology inside your own walls. You are asking your staff to use tools that many of them distrust, to produce work for an audience that’s increasingly skeptical of it, and to do it without losing the credibility that makes your journalism worth anything in the first place.

That is a tension most AI coverage glosses over. The trade publications write about the tools. The think pieces write about the ethics. Almost nobody writes about what it actually feels like to be a news director in 2026 trying to thread that needle every day.

I’ve been in those rooms. Here’s what I’ve seen work and what hasn’t.

The newsrooms that are getting this wrong treat AI implementation as a technology project. They bring in a vendor, run a pilot, announce the tool in a staff meeting and wonder why adoption is slow and morale is worse. They skip the conversation that actually needs to happen first: what are we afraid of, and what are we trying to protect?

The newsrooms getting it right treat it as a journalism question. What does this tool let us do that serves our audience better? What does it not touch — and why? Where is the line between AI-assisted work and AI-generated work, and how do we explain that line to our audience clearly?

The second approach takes longer up front. It involves harder conversations. But it produces something the first approach never does: staff who are invested in the outcome rather than waiting to be replaced by it.

Pichai used an example in the interview that stuck with me. He described asking an AI agent to look ahead at his calendar and color-code meetings by category. It took seconds. It probably would have taken him or an assistant 20 minutes to do manually.

That is the version of AI that most newsroom staff can accept — a tool that handles the administrative noise so they can do more actual journalism. The version they can’t accept, and won’t, is one that feels like it’s being used to justify cutting the people doing the journalism.

The distinction isn’t about the technology. It’s about the intent, and whether leadership communicates it clearly.

The 35 percent number isn’t going to fix itself. Public trust in AI will shift based on whether the technology demonstrably improves people’s lives — and whether institutions like news organizations handle it in ways their audiences can respect.

That means being transparent about where and how you’re using it. It means not hiding AI-assisted work inside a byline that implies otherwise. It means giving your staff a voice in how it gets implemented, not just a training session after the decision is already made.

Pichai said anxiety is healthy; in a democracy, you want citizens engaged and making their preferences known. That’s true. It’s also true inside your newsroom. The journalists who are worried about what AI means for their jobs — they’re not wrong to be worried. The news directors who listen to that and respond thoughtfully will build something durable. The ones who don’t will have a different set of problems in about 18 months.

AI Ethics in Journalism: Beyond Human Baseline

The “human baseline” approach posits that the ethical success of artificial intelligence is achieved when its decision-making mirrors or marginally improves upon that of a competent human.  In the classic “trolley problem,” this implies that if an AI can consistently choose the “lesser of two evils” with more precision than a panicked human, it has cleared the ethical bar.

However, as the media and journalism industry increasingly integrates generative AI and automated editorial systems, it is becoming clear that a “slightly better than human” standard is insufficient. In the context of information dissemination, a human-level baseline for AI is not a gold standard; it is a liability.

While comparing AI to the human baseline in moral dilemmas reveals the machine’s capacity for consistency, it fails to account for the unique accountability required in journalism.  

Because audiences in 2026 are caught in a “breaking verification” crisis where trust is the ultimate currency, an AI that is merely “slightly better” than a biased human is ethically insufficient. To be truly ethical, AI in media must move beyond mimicking human choice to provide a level of transparency and evidentiary rigor that transcends a journalist’s capability.

Our newsrooms are facing a speed-versus-verification dilemma.   The human baseline for a journalist is breaking the story vs. being 100% accurate.   AI’s logic is fundamentally different.   AI shifts control from individual journalists to automated systems optimized for engagement and scalability.   Therefore, an AI that performs ‘slightly better’ than a journalist at producing content quickly may be ethically inferior if its underlying logic lacks the transparency and evidentiary rigor that defines journalistic integrity.

Because so much information is published in many ways across many platforms, audiences are having a difficult time distinguishing fact from fiction. 

“‘Breaking verification’ will replace ‘breaking news’ in 2026, and trust will decide who survives,” according to Vinay Sarawagi, co-founder and CEO of The Media GCC.

Audiences need to see evidence and sources to back up what they see online, because seeing is no longer believing.   If AI only does as well as humans at spotting fakes, it’s not enough. To solve the trust crisis, the AI must be exponentially better at citing sources.

In 2005, Wallach and Allen argued that the principal goal of the discipline of artificial morality is to design artificial agents to act as if they are moral agents. They distinguish between operational morality, in which an AI simply follows pre-programmed human safety rules, and functional morality, in which a system can independently navigate moral dilemmas.  In journalism, an AI that merely mirrors an editor’s baseline choices is operating within a limited framework.   If the media is to serve the public’s best interests, a journalist AI must move toward a functional morality that transcends basic human instinct and provides the transparency and accountability the public expects.

From a strategic standpoint, “slightly better” is a recipe for disaster.   If AI-generated content results in a libel suit or negatively impacts a company’s stock price, the defense that AI is slightly more accurate than an average human is a losing argument.  As the media shifts into what is being termed the ‘Answer Economy’, the traditional value proposition of a newsroom is being disrupted. When AI models synthesize reports into a single summary, the value of a news organization is no longer just the ‘answer’ or the scoop itself, but the auditable trail of evidence that allows that answer to be verified (Seo Ai Club, 2026). If an AI only meets the human baseline for producing a plausible-sounding summary without providing this rigorous, machine-readable proof of its sources, it fails to meet the ethical demands of a 2026 audience.

Note: This is an essay originally written for a course on AI and business strategy at Johns Hopkins University.

References

Wallach, Wendell and Allen, Colin. “Artificial Morality: Top-down, Bottom-up, and Hybrid Approaches.” Ethics and Information Technology volume 7, no. issue 3 (September 2005): 149-155. https://link.springer.com/article/10.1007/s10676-006-0004-4.

Li, Haoran et al. “Artificial Intelligence and Journalistic Ethics: A Comparative Analysis.” Journal of Journalism and Media volume 6, no. issue 3 (August 2025): 105. https://www.mdpi.com/2673-5172/6/3/105.

Mee, S. et al. “Moral judgments of human vs. AI agents in moral dilemmas.” Scientific Reports volume 13, no. issue 1 (February 2023). https://pmc.ncbi.nlm.nih.gov/articles/PMC9951994/.

Simon, Felix.How AI reshapes editorial authority in journalism.” Digital Content Next (June 2025)

Reuters Institute.How will AI reshape the news in 2026? Forecasts by 17 experts around the world.” Reuters Institute for the Study of Journalism (January 2025)

Seo Ai Club.The Answer Economy: A Comprehensive Analysis of Answer Engine Optimization Tracking Software and Strategic Market Leadership.” Seo Ai Club (January 2025)

Is going viral my goal?

One of the most famous viral videos of all-time is “Charlie bit my finger” with 870M views, and that video and other early videos helped create an atmosphere of “I want to go viral”

So what does that mean anyway – going viral?   Is it 100,000 views, 5 million of 870 million?    There’s no hard and fast definition.

A video can get a million views because a brand paid to have it places on various sites, so if the video is seeded with paid support and emails and other means is it truly viral?

There is a certain mindset that failing to go viral means your social media campaign is a failure, and I don’t buy into that nonsense.

“Going viral” misses the point of brand messaging which is reaching the right audience and building a relationship with them by producing content that is useful and actionable for them.    I can buy and spend and clickbait my way to a million views, but how many watch beyond 10 seconds and how many took action in a positive way towards my brand?

Every video you create is not a one-time shot at going viral, but an investment in a long-term relationship with your customers.

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