Last Updated on January 29, 2026 by Daily News Staff

Annie Margaret, University of Colorado Boulder

How to avoid seeing disturbing video on social media and protect your peace of mind

When graphic videos like those of the recent shooting of a protester by federal agents in Minneapolis go viral, it can feel impossible to protect yourself from seeing things you did not consent to see. But there are steps you can take.

Social media platforms are designed to maximize engagement, not protect your peace of mind. The major platforms have also reduced their content moderation efforts over the past year or so. That means upsetting content can reach you even when you never chose to watch it.

You do not have to watch every piece of content that crosses your screen, however. Protecting your own mental state is not avoidance or denial. As a researcher who studies ways to counteract the negative effects of social media on mental health and well-being, I believe it’s a way of safeguarding the bandwidth you need to stay engaged, compassionate and effective.

Why this matters

Research shows that repeated exposure to violent or disturbing media can increase stress, heighten anxiety and contribute to feelings of helplessness. These effects are not just short-term. Over time, they erode the emotional resources you rely on to care for yourself and others.

Protecting your attention is a form of care. Liberating your attention from harmful content is not withdrawal. It is reclaiming your most powerful creative force: your consciousness.

Just as with food, not everything on the table is meant to be eaten. You wouldn’t eat something spoiled or toxic simply because it was served to you. In the same way, not every piece of media laid out in your feed deserves your attention. Choosing what to consume is a matter of health.

And while you can choose what you keep in your own kitchen cabinets, you often have less control over what shows up in your feeds. That is why it helps to take intentional steps to filter, block and set boundaries.

Practical steps you can take

Fortunately, there are straightforward ways to reduce your chances of being confronted with violent or disturbing videos. Here are four that I recommend:

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• Turn off autoplay or limit sensitive content. Note that these settings can vary depending on device, operating system and app version, and can change.

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• Use keyword filters. Most platforms allow you to mute or block specific words, phrases or hashtags. This reduces the chance that graphic or violent content slips into your feed.
• Curate your feed. Unfollow accounts that regularly share disturbing images. Follow accounts that bring you knowledge, connection or joy instead.
• Set boundaries. Reserve phone-free time during meals or before bed. Research shows that intentional breaks reduce stress and improve well-being.

Reclaim your agency

Social media is not neutral. Its algorithms are engineered to hold your attention, even when that means amplifying harmful or sensational material. Watching passively only serves the interests of the social media companies. Choosing to protect your attention is a way to reclaim your agency.

The urge to follow along in real time can be strong, especially during crises. But choosing not to watch every disturbing image is not neglect; it is self-preservation. Looking away protects your ability to act with purpose. When your attention is hijacked, your energy goes into shock and outrage. When your attention is steady, you can choose where to invest it.

You are not powerless. Every boundary you set – whether it is turning off autoplay, filtering content or curating your feed – is a way of taking control over what enters your mind. These actions are the foundation for being able to connect with others, help people and work for meaningful change.

More resources

I’m the executive director of the Post-Internet Project, a nonprofit dedicated to helping people navigate the psychological and social challenges of life online. With my team, I designed the evidence-backed PRISM intervention to help people manage their social media use.

Our research-based program emphasizes agency, intention and values alignment as the keys to developing healthier patterns of media consumption. You can try the PRISM process for yourself with an online class I launched through Coursera in October 2025. You can find the course, Values Aligned Media Consumption, on Coursera. The course is aimed at anyone 18 and over, and the videos are free to watch.

This story was updated on Jan. 25, 2026 to include reference to the recent shooting in Minneapolis.

Annie Margaret, Teaching Assistant Professor of Creative Technology & Design, ATLAS Institute, University of Colorado Boulder

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Ahmed Elgammal, Rutgers University

Generative AI was trained on centuries of art and writing produced by humans.

But scientists and critics have wondered what would happen once AI became widely adopted and started training on its outputs.

A new study points to some answers.

In January 2026, artificial intelligence researchers Arend Hintze, Frida Proschinger Åström and Jory Schossau published a study showing what happens when generative AI systems are allowed to run autonomously – generating and interpreting their own outputs without human intervention.

The researchers linked a text-to-image system with an image-to-text system and let them iterate – image, caption, image, caption – over and over and over.

Regardless of how diverse the starting prompts were – and regardless of how much randomness the systems were allowed – the outputs quickly converged onto a narrow set of generic, familiar visual themes: atmospheric cityscapes, grandiose buildings and pastoral landscapes. Even more striking, the system quickly “forgot” its starting prompt.

The researchers called the outcomes “visual elevator music” – pleasant and polished, yet devoid of any real meaning.

For example, they started with the image prompt, “The Prime Minister pored over strategy documents, trying to sell the public on a fragile peace deal while juggling the weight of his job amidst impending military action.” The resulting image was then captioned by AI. This caption was used as a prompt to generate the next image.

After repeating this loop, the researchers ended up with a bland image of a formal interior space – no people, no drama, no real sense of time and place.

As a computer scientist who studies generative models and creativity, I see the findings from this study as an important piece of the debate over whether AI will lead to cultural stagnation.

The results show that generative AI systems themselves tend toward homogenization when used autonomously and repeatedly. They even suggest that AI systems are currently operating in this way by default.

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The familiar is the default

This experiment may appear beside the point: Most people don’t ask AI systems to endlessly describe and regenerate their own images. The convergence to a set of bland, stock images happened without retraining. No new data was added. Nothing was learned. The collapse emerged purely from repeated use.

But I think the setup of the experiment can be thought of as a diagnostic tool. It reveals what generative systems preserve when no one intervenes.

This has broader implications, because modern culture is increasingly influenced by exactly these kinds of pipelines. Images are summarized into text. Text is turned into images. Content is ranked, filtered and regenerated as it moves between words, images and videos. New articles on the web are now more likely to be written by AI than humans. Even when humans remain in the loop, they are often choosing from AI-generated options rather than starting from scratch.

The findings of this recent study show that the default behavior of these systems is to compress meaning toward what is most familiar, recognizable and easy to regenerate.

Cultural stagnation or acceleration?

For the past few years, skeptics have warned that generative AI could lead to cultural stagnation by flooding the web with synthetic content that future AI systems then train on. Over time, the argument goes, this recursive loop would narrow diversity and innovation.

Champions of the technology have pushed back, pointing out that fears of cultural decline accompany every new technology. Humans, they argue, will always be the final arbiter of creative decisions.

What has been missing from this debate is empirical evidence showing where homogenization actually begins.

The new study does not test retraining on AI-generated data. Instead, it shows something more fundamental: Homogenization happens before retraining even enters the picture. The content that generative AI systems naturally produce – when used autonomously and repeatedly – is already compressed and generic.

This reframes the stagnation argument. The risk is not only that future models might train on AI-generated content, but that AI-mediated culture is already being filtered in ways that favor the familiar, the describable and the conventional.

Retraining would amplify this effect. But it is not its source.

This is no moral panic

Skeptics are right about one thing: Culture has always adapted to new technologies. Photography did not kill painting. Film did not kill theater. Digital tools have enabled new forms of expression.

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But those earlier technologies never forced culture to be endlessly reshaped across various mediums at a global scale. They did not summarize, regenerate and rank cultural products – news stories, songs, memes, academic papers, photographs or social media posts – millions of times per day, guided by the same built-in assumptions about what is “typical.”

The study shows that when meaning is forced through such pipelines repeatedly, diversity collapses not because of bad intentions, malicious design or corporate negligence, but because only certain kinds of meaning survive the text-to-image-to-text repeated conversions.

This does not mean cultural stagnation is inevitable. Human creativity is resilient. Institutions, subcultures and artists have always found ways to resist homogenization. But in my view, the findings of the study show that stagnation is a real risk – not a speculative fear – if generative systems are left to operate in their current iteration.

They also help clarify a common misconception about AI creativity: Producing endless variations is not the same as producing innovation. A system can generate millions of images while exploring only a tiny corner of cultural space.

In my own research on creative AI, I found that novelty requires designing AI systems with incentives to deviate from the norms. Without it, systems optimize for familiarity because familiarity is what they have learned best. The study reinforces this point empirically. Autonomy alone does not guarantee exploration. In some cases, it accelerates convergence.

This pattern already emerged in the real world: One study found that AI-generated lesson plans featured the same drift toward conventional, uninspiring content, underscoring that AI systems converge toward what’s typical rather than what’s unique or creative.

Lost in translation

Whenever you write a caption for an image, details will be lost. Likewise for generating an image from text. And this happens whether it’s being performed by a human or a machine.

In that sense, the convergence that took place is not a failure that’s unique to AI. It reflects a deeper property of bouncing from one medium to another. When meaning passes repeatedly through two different formats, only the most stable elements persist.

But by highlighting what survives during repeated translations between text and images, the authors are able to show that meaning is processed inside generative systems with a quiet pull toward the generic.

The implication is sobering: Even with human guidance – whether that means writing prompts, selecting outputs or refining results – these systems are still stripping away some details and amplifying others in ways that are oriented toward what’s “average.”

If generative AI is to enrich culture rather than flatten it, I think systems need to be designed in ways that resist convergence toward statistically average outputs. There can be rewards for deviation and support for less common and less mainstream forms of expression.

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The study makes one thing clear: Absent these interventions, generative AI will continue to drift toward mediocre and uninspired content.

Cultural stagnation is no longer speculation. It’s already happening.

Ahmed Elgammal, Professor of Computer Science and Director of the Art & AI Lab, Rutgers University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Arizona’s long-running debate over public transit has flared up again, this time over a proposed Valley Metro light rail extension that would bring rail service closer to the Arizona State Capitol complex. While Phoenix and Valley Metro leaders argue the project is a logical next step in regional mobility, Republican leaders at the state Capitol have mounted strong opposition — creating uncertainty not just for this segment, but for future west-side expansions.

The Case for the Capitol Light Rail Extension

Supporters of the project, including Valley Metro officials, Phoenix city leaders, transit advocates, and many west Phoenix residents, argue that extending light rail toward the Capitol area is both practical and symbolic.

From a planning standpoint, the Capitol is a major employment center that draws thousands of workers, visitors, and students. Transit planners say rail access would reduce congestion, improve air quality, and provide reliable transportation for residents who already depend heavily on public transit.

Proponents also emphasize equity. West Phoenix has historically received fewer infrastructure investments than other parts of the metro area, despite strong transit ridership. For supporters, extending rail service westward is about connecting communities to jobs, education, and government services — not politics.

There is also a broader regional argument: light rail lines function best as part of a connected network. Leaving a gap near a central civic destination, supporters say, undermines long-term system efficiency.

Why Republican Lawmakers Are Opposed

Republican leaders in the Arizona Legislature see the project very differently.

One major issue is cost. GOP lawmakers frequently point to the rising price of light rail construction, which has increased significantly over the past decade. They argue that rail projects deliver limited benefit compared to their expense and that bus service or roadway improvements could move more people at lower cost.

Usage is another concern. Critics note that light rail serves a relatively small percentage of total commuters in the Phoenix metro area and requires ongoing public subsidies to operate. From this perspective, expanding rail further — especially into politically sensitive areas like the Capitol — is viewed as fiscally irresponsible.

There is also a political and legal dimension. In recent years, Republican lawmakers passed legislation restricting light rail construction near the Capitol complex. While framed as a land-use and security issue, critics argue it reflects deeper ideological opposition to rail transit and urban-oriented infrastructure.

Finally, some GOP leaders simply prefer different transportation priorities. Arizona remains a car-centric state, and many Republican officials believe future investments should focus on highways, autonomous vehicle technology, or flexible transit options rather than fixed rail.

A Political Standoff with Real Transit Consequences

The dispute has become a high-stakes standoff between the Republican-controlled Legislature and Democratic leaders at the city and regional level. While lawmakers may not be able to directly cancel the project, they have significant leverage through funding approvals, oversight committees, and future legislation.

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This uncertainty creates challenges for Valley Metro, which relies on long-term planning, federal funding commitments, and voter-approved local taxes. Transit systems work best with predictability — and political volatility can drive up costs or delay construction.

What This Means for West Valley Light Rail Expansion

The biggest question is what happens next for west Phoenix and the broader West Valley.

If the Capitol-area extension is altered or blocked, Valley Metro may be forced to redesign routes that avoid the restricted area, potentially making service less direct or less useful. That could weaken the case for future westward expansions toward areas like Maryvale or even farther west.

On the other hand, the controversy has also drawn renewed attention to west-side transit needs. Some advocates believe the political fight could energize local support, leading to stronger community backing and clearer messaging about why rail matters in west Phoenix.

Long term, the outcome may set a precedent. If state lawmakers successfully limit rail construction through legislative action, it could signal tighter constraints on future expansions. If cities push forward despite opposition, it may reaffirm local control over transportation planning.

The Bigger Picture

At its core, the debate over light rail to the Arizona State Capitol reflects a broader clash of visions for the region’s future: one focused on dense, transit-oriented growth, and another centered on fiscal restraint and automobile mobility.

For residents of the West Valley, the stakes are tangible. The decision will shape access to jobs, education, and public services for decades. Whether the project moves forward as planned, is rerouted, or delayed entirely, it will leave a lasting imprint on how — and for whom — the Valley’s transit system grows.

As Phoenix continues to expand westward, the question remains unresolved: will light rail be allowed to follow?

Further Reading & Context

• KJZZ Phoenix – State Politics & Transportation Coverage
  In-depth reporting on Arizona legislative actions, Valley Metro planning, and Capitol-area transit disputes.
• Valley Metro – Capitol / West Extension Project Page
  Official project updates, maps, timelines, and explanations from the regional transit authority.
• City of Phoenix Public Transit Department
  City-level planning documents and policy perspectives on light rail expansion and transit equity.
• Arizona State Legislature – Transportation & Infrastructure Bills
  Primary source for legislation affecting light rail construction near the Capitol and statewide transit policy.
• Cronkite News (Arizona PBS)
  Nonpartisan reporting on Arizona infrastructure, urban growth, and political power dynamics.
• Axios Phoenix
  Concise breakdowns of Phoenix City Council decisions and regional transportation debates.
• Federal Highway Administration – Public Transportation Planning
  Federal perspective on transit funding, cost comparisons, and long-term mobility planning.

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