How to Identify and Combat Misinformation in the Digital Age

By Hugo Villar, University of California San Diego Division of Extended Studies.

Fact-Checked by Irfan Ahmad, DIW. 

Image: Insaanu Studio - Unsplash

The ability to identify and combat misinformation has become an essential skill for navigating our information-rich world. With the proliferation of social media platforms, AI-generated content, and the speed at which information spreads, distinguishing between reliable and misleading information requires systematic approaches and critical thinking skills that go beyond surface-level assessment.

There are increasing challenges for instructors, students, journalists, professionals, and everyday internet users in navigating today's complex information landscape, and the consequences can be severe. Whether you're a student writing a course assignment essay, a journalist verifying information, a professional preparing a publication or report, or a reader navigating online content, the ability to spot false or misleading information has never been more critical to maintaining integrity and avoiding the very serious negative outcomes of faulty work.

Understanding the Psychology of Misinformation

Misinformation succeeds largely because it exploits fundamental psychological mechanisms that influence how we process information. Research reveals that false information often appeals to emotions rather than logic, utilizing highly emotional or sensational content that captures attention and sticks in memory. This emotional manipulation bypasses our rational scrutiny, particularly when we encounter content that makes us angry, fearful, or overjoyed.

Several cognitive biases make us vulnerable to misinformation:

• Confirmation bias leads us to favor information that confirms our existing beliefs while becoming skeptical of contradictory evidence.
• The illusory truth effect occurs when repeated exposure to false statements makes them seem more believable, a phenomenon particularly dangerous in social media environments where the same false claims may appear repeatedly from different sources. Additionally, we tend to trust information shared by friends or people we respect, creating echo chambers that reinforce one-sided narratives.

Ways to Spot Misinformation

There are several tests that can help in spotting misinformation, as well as several tools to identify misleading sources and fact-checking.

The CRAAP Test: A Foundational Framework

The CRAAP test provides a systematic approach to source evaluation, examining five key criteria.

• Currency involves assessing when the source was published and whether it remains current enough for your research needs, crucial for fields like technology and media.
• Relevance determines whether the article provides sufficient information to support your understanding or offers meaningful counter-arguments.
• Authority examines the author's reputation, credentials, and institutional affiliations, with educational institutions generally being less likely to include bias than political organizations.
• Accuracy focuses on whether the source is supported by data or evidence, while also considering the quality of writing, spelling, and grammar.
• Finally, Point of view or Purpose helps you understand whether the author uses facts or opinions and whether they're trying to sell something or educate you. However, it's important to note that sources can pass the CRAAP test and still be inappropriate for your research, underscoring the need to combine this framework with other evaluation strategies.

The SIFT Method: Quick Decision-Making for Digital Content

Developed by digital literacy expert Mike Caulfield, the SIFT method provides four steps for evaluating online content.

• The first step, Stop, emphasizes pausing before reading, sharing, or using a source until you can verify its reliability. This step is particularly crucial given how social media platforms and news organizations deliberately promote sensational, emotionally charged content designed to capture attention.
• Investigate the Source involves taking a moment to research the author and publication, examining their mission, potential biases, and authority in the relevant field.
• Find Better Coverage encourages lateral reading to determine whether other sources corroborate or dispute the information, including checking whether fact-checkers have already analyzed the claims.
• Finally, Trace Claims, Quotes, and Media involve following references back to their original sources to verify the accuracy of reported information.

Lateral Reading: The Professional Approach

Lateral reading represents a fundamental shift from traditional source evaluation methods. Instead of relying solely on the characteristics of a single source (vertical reading), lateral reading involves opening multiple browser tabs to research what other credible sources say about the original source and its claims. This technique, used by professional fact-checkers, provides a more complete picture of a source's credibility than examining it in isolation.

The practice involves leaving the original webpage to verify information with external references, checking the author's credentials across multiple platforms, and examining the publication's peer-review practices or editorial standards. Research from Stanford University demonstrates that lateral reading can help users assess the credibility of online information more effectively.

Identifying Red Flags and Warning Signs

Image: Igor Omilaev - Unsplash

Certain characteristics consistently appear in misinformation and should trigger immediate skepticism.

• Emotional manipulation is a primary red flag - content that uses simplistic or emotionally charged language for complex issues, employs stereotypes without context, or uses sensational headlines rather than focusing on facts.
• Technical indicators include spelling errors, grammatical mistakes, low-resolution images, or images that appear to have been manipulated.
• Structural red flags involve articles lacking author attribution, sources that cannot be verified, or "About Us" pages that reveal a lack of credentials.
• Content warnings include claims that seem too shocking, good, or strange to be true; references to incorrect or outdated information; and clickbait headlines designed to generate emotional responses rather than inform.

Advanced Verification Techniques

Modern misinformation detection requires sophisticated verification tools and techniques.

• Reverse image searching using Google's "About This Image" feature can reveal whether images have been manipulated, taken out of context, or recycled from previous events. This tool provides information about an image's origins, age, and where it has appeared across the web.
• Cross-referencing and triangulation involve verifying information across multiple reliable sources, including traditional media outlets and library databases. Fact-checking websites like FactCheck.org, Snopes, and PolitiFact provide professional verification of questionable claims.
• Source chain verification requires tracing claims back to their original sources, examining the methodology behind studies or surveys, and ensuring that secondary reporting accurately represents primary research.

Digital Tools for Fact-Checking

Several technological tools can help identify misinformation.

• Google's Fact Check Explorer functions as a search engine specifically for fact-checks, allowing users to search by keyword, person, or topic to find existing debunking efforts.
• MediaVault provides specialized archiving for fact-checkers, preserving images and videos that often disappear from social media after being analyzed.
• ClaimReview markup helps identify professionally fact-checked content by tagging articles with structured data that search engines and social media platforms use to promote verified information. These tools are part of a growing ecosystem designed to help users distinguish between reliable and misleading information in real time.

The AI Challenge: When Machines Misinform

Large Language Models (LLMs) like ChatGPT, Perplexity, Claude, and Gemini and all other tools within this class of AI, introduce a new category of misinformation through hallucinations, when AI systems generate false or misleading information that appears credible. These hallucinations occur because LLMs generate responses based on statistical patterns in training data rather than true understanding of facts. Research shows hallucination rates can be high in medical contexts, with one study finding an overall rate of 65.9% across six AI models tested on clinical cases.

AI misinformation differs from human-generated false information in several key ways. Training data often contains biases, omissions, or inconsistencies that embed systematic flaws into outputs. The training process remains largely opaque, making it difficult to audit why models produce specific outputs. Additionally, downstream filtering struggles to detect subtle hallucinations due to volume and context-sensitivity concerns.

Safe AI Usage Practices require that AI-generated content be treated with appropriate skepticism. Never share personal, confidential, or sensitive information with AI systems, as interactions are not private and may be used to improve the model. Always cross-check AI-provided information with reliable sources, especially for factual claims, statistics, or specialized knowledge. Be aware that AI systems reflect the biases present in their training data and cannot verify the accuracy of their outputs.

Combating Emotional Manipulation

Understanding how misinformation exploits emotions is crucial for developing resistance strategies.

• Emotional awareness involves recognizing when content is designed to provoke strong emotional responses that cloud judgment.
• Pause-and-reflect techniques encourage taking time before sharing or believing information that triggers intense feelings such as anger, fear, or outrage.
• Critical questioning involves asking why someone would create specific content, what their motivation might be, and whether the emotional response you're experiencing is proportionate to the actual facts presented. Research demonstrates that fake news articles use significantly more emotional language than legitimate news articles, particularly negative emotions designed to capture attention and encourage sharing.

Building Information Resilience

Developing long-term resistance to misinformation requires cultivating critical-thinking habits and information-literacy skills.

• Diversifying information sources helps prevent echo chamber effects by exposing you to multiple perspectives on important issues.
• Regular fact-checking habits involve making verification a routine part of information consumption, especially before sharing content on social media.
• Understanding information ecosystems involves recognizing how algorithms influence the content you see and how your engagement patterns shape future recommendations.
• Continuous learning about new forms of misinformation, such as deepfakes and AI-generated content, ensures your detection skills remain current with emerging threats.

The fight against misinformation requires both individual vigilance and collective responsibility. By mastering these evaluation frameworks, understanding psychological vulnerabilities, and utilizing available verification tools, we can build more resilient information communities.

The stakes are significant - misinformation can undermine democratic processes, erode trust in institutions, discourage preventive health behaviors, and contribute to societal polarization. However, with systematic approaches to information evaluation and a commitment to critical thinking, we can maintain the integrity of public discourse while making well-informed decisions in our personal and professional lives.

Editor's Note: The title and some wording in the article have been edited for clarity and broader audience relevance, and relevant links have been added.

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by External Contributor via Digital Information World

Is the nuclear non‑proliferation pact a model for regulating runaway AI?

Nicholas Ross Smith, University of Waikato

Image: Stephen Cobb - Unsplash

The potential existential threat of artificial intelligence has leapt into the headlines after a departing safety researcher at AI corporation Anthropic posted on social media he believed there was more than a one-in-ten chance AI could kill everyone.

Other Anthropic insiders went public in support of his views, saying the technology could become advanced and dangerous enough to cause human extinction within ten years.

This all followed renewed warnings from Turing Award-winning computer scientist Yoshua Bengio, who has spent the past year arguing that increasingly autonomous AI systems could develop self-preservation goals humans cannot easily switch off.

Even if the current development of AI falls short of artificial superintelligence or artificial general intelligence, many have warned it might be used by bad actors for nefarious ends.

This includes biological weapons design, cyber-attacks against critical infrastructure and financial systems, and assisting hybrid warfare efforts. More conventionally, we already know AI was used in the planning and execution of the United States raid on Venezuela at the start of this year.

A number of sceptics have argued the latest claims remain highly speculative. But there are still sound reasons to apply the cautionary principle to a technology that is developing at an exponential rate.

Of particular concern is that AI is emerging as central to growing competition between the US and China. Echoing past space and nuclear races, both superpowers fear falling behind, making any form of restraint a strategic risk rather than a virtue.

We have been here before.

Building the nuclear taboo

Early in the Cold War arms race, nuclear weapons were treated like an offensive tactical option. After a number of critical defeats during the first year of the Korean War (after China sided with North Korea in 1950), key US military figures – notably General Douglas MacArthur and Secretary of State John Foster Dulles – saw nuclear weapons as a way to regain control.

Fortunately, calmer heads prevailed. But the fact they were seen as a legitimate option shows how relatively recent nuclear arms control is.

That taboo against the use of nuclear weapons had to be built. And it was built out of fear, not goodwill.

It took the Soviet Union closing the technology gap – first with its own atomic test in 1949, then with the development of thermonuclear weapons in the 1950s – for the mentality on both sides to begin to shift.

Only once both powers had something to lose did nuclear weapons start being treated less as tools of war-fighting and more as instruments of deterrence. They became seen as a defensive last resort rather than an offensive first move.

The Cuban Missile Crisis in 1962 is often remembered as the closest the world came to nuclear Armageddon. And while the stakes were incredibly high, given both sides were developing nuclear “triad” capabilities involving aircraft, submarines and intercontinental ballistic missiles, it also helped alter the way the US and the Soviet Union thought about nuclear weapons.

In 1963, the Moscow-Washington hotline was implemented and the Limited Nuclear Test Ban Treaty was signed. This helped usher in a period of relative geopolitical calm known as “détente”.

In 1970, the Treaty on the Non-Proliferation of Nuclear Weapons came into force. While less than perfect – several nuclear weapons states are not signatories – the treaty showed that a degree of international cooperation on a crucial problem could be achieved.

This didn’t end the rivalry between Washington and Moscow, nor did it stop proxy wars from Vietnam to Afghanistan. But it did establish a base line: direct superpower confrontation, and with it the risk of nuclear escalation, became something both sides worked to avoid.

When the stakes are high

If the accelerating development of AI poses a similarly existential risk – and enough people building the technology now say it might – the hope has to be that Washington and Beijing eventually treat the issue with something like the seriousness previously applied to nuclear weapons.

That would mean moving past mutual suspicion towards a basic, shared recognition that an uncontrolled AI race benefits neither side if the technology itself is the risk.

None of this means the potential threat posed by AI will dissipate completely. In the case of nuclear weapons, despite the aspiration of the Non-Proliferation Treaty, rogue nuclear states emerged and intensifying rivalry between the US, China and Russia has seen the gradual erosion of those frameworks.

But if there is a lesson worth drawing from the first nuclear age, it is that even bitter rivals can find their way to a shared understanding once the stakes became unmistakably mutual.

Getting there sooner rather than after a crisis would be the more responsible path this time.

Nicholas Ross Smith, Senior Lecturer in International Relations, University of Waikato

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

Edited by Irfan Ahmad.

Read next: Internet Archive’s Wayback Machine Blocks Some Real Users Amid High-Volume Bot Traffic

by External Contributor via Digital Information World

Internet Archive’s Wayback Machine Blocks Some Real Users Amid High-Volume Bot Traffic

The Internet Archive said September 15 that the Wayback Machine has been hit by waves of high-volume automated traffic, prompting it to put protections in place to keep the service running.

The archive said some of those protections have mistakenly blocked real users.

The archive also recently changed the message users see when a request is blocked with a 429 error. The error means "too many requests."

The Internet Archive said it is improving its ability to distinguish abusive bots from people who use the Wayback Machine. Users who believe they were blocked by mistake can email info@archive.org with their operating system, browser and IP address so the archive can investigate.

The update was posted by Mark Graham, director of the Wayback Machine, who apologized for the errors and thanked users for their patience.

Screenshot: DIW. CC BY

Fact-Checked by Irfan Ahmad.

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Researchers Find Depression Correlation Among Adults Spending 150 Minutes Daily on Social Media

By Michael Miller, Email Michaelm, University of Cincinnati

Image: Shiju B - Unsplash

U.S. News & World Report, USA Today and other national media highlighted a study by the University of Cincinnati and Northwestern University that found a correlation between heavy daily social media use and depression.

Social media use is a better predictor of depression than many other common factors such as one’s job and educational background or time spent on other activities such as playing video games, surfing the web or watching TV.

Meanwhile, researchers observed a correlation with self-reported depression in survey respondents starting at 2.5 hours of daily scrolling.

Researchers assessed 11 years of data from the annual Media Behaviors and Influence Study starting in 2014 and found that social media use was among the most important predictors of self-reported depression.

“When it comes to depression and social media, the argument is salient across scientific test after test of those 11 years,” corresponding author and UC Professor Hans Breiter said.

“It’s a damning picture. Social media is harmful for individuals across the lifespan. We should not subject ourselves to a product using addiction science to optimize our viewing time.“

The study was published in the Nature Portfolio journal Mental Health Research.

UC and Northwestern analyze 183,300 adults over 11 years

UC researchers collaborated with co-lead author and Professor Emeritus Martin Block at Northwestern University. While numerous studies have documented associations between social media use and negative outcomes such as mental health conditions, especially depression, none have used AI to show these relationships persist in adults.

Researchers examined 183,300 anonymized surveys from adults ages 18 to 70 from the Media Behaviors and Influence Study conducted annually since 2002 by Prosper Insights & Analytics. The survey asks 1,318 questions across topics and the respondent’s health across 31 measures, including self-reported depression.

They used a machine learning model that was evaluated with tools designed to ensure its accuracy and calibration. Using this model, they found an increasing relationship between social media use and depression.

“Much of the published research has focused on younger populations, single datasets or broad measures of screen time. We need studies that distinguish social media from other digital activities and examine whether the same associations appear across different populations and years,” co-lead author and UC Research Associate Vikram Suresh said.

Researchers observed a correlation with self-reported depression among respondents who spent at least 150 minutes on social media per day. They observed the same result in each of the 11 years studied.

“Mental health and happiness are about engaging the world and having purpose, not passively watching something for hours,” Breiter said.

UC doctoral student and study co-author Johnathan Avant said people long have suspected that excessive social media use could have negative health effects.

“You can see trends on social media where they talk about the mental health toll it has on them,” Avant said.

Researchers found that social media increasingly became a bigger predictor of depression during the 11 years studied, eclipsing many other factors like TV viewing or web surfing.

UC senior research associate and co-author Nicole Vike said consumption of social media has changed over the years.

“Initially, social media was a social outlet with people you knew,” Vike said.

Now, people typically consume content made by strangers, she said.

“It’s crazy how addictive it can be. Look at chronically online content creators,” Vike said. “They often have to take a monthslong hiatus from being online for their mental health.”

Is it realistic to quit social media?

The study was supported with grants from the Office of Naval Research and a donation from UC alumnus Jim Goetz.

While the $300 billion industry has become ubiquitous in daily life, UC’s senior research associate and co-author Sumra Bari said there are ways people can limit time spent on social media like setting timers, turning off notifications and fully logging out of accounts after every session to make it harder to scroll again out of habit.

But it might be unrealistic for people to simply turn it off, she said.

“There is a lot of social pressure to stay connected and be out there. So being aware of the risks as early as possible is important,” she said.

Professor emeritus Block noted, “Social media is a common marker of our cultural age much like the automobile was more than a century ago. But social media has an important negative effect through its association with depression, much like the automobile is associated with injuries from crashes and pollution.”

Breiter said their findings suggest it’s in society’s best interest to find ways to mitigate the harms of today’s deeply online culture.

“This is a wake-up call,” he said.

Fact-Checked by Irfan Ahmad.

Read next: AI is supercharging money scams – here’s what you can do to protect yourself
by External Contributor via Digital Information World

AI is supercharging money scams – here’s what you can do to protect yourself

Pawan Jain, University of Michigan Flint

Image: Vitaly Gariev - Unsplash

The phone rings, and it’s your grandson’s shaken voice. There’s been an accident, he says, and he needs money before anyone finds out. Except it isn’t him. It’s software that learned his voice from a clip posted online, run by a stranger working through a list of phone numbers.

For years, warnings about artificial intelligence and cybersecurity have focused on corporate networks and government systems. Those threats are real. But if you follow the money in the FBI’s fraud data, a different picture emerges: Staggering sums are flowing out of household accounts.

In its 2025 annual report, the FBI’s Internet Crime Complaint Center began tracking complaints with an AI connection for the first time. Americans filed more than 22,000 such cases and reported roughly US$893 million in losses. Investment fraud accounted for $632 million of that, and people over 60 accounted for $352 million of the losses.

Those figures include only victims who reported to the FBI, and only cases where AI’s role could be identified. The consulting firm Deloitte projects that the actual hit from AI will be much bigger and help push U.S. fraud losses overall to $40 billion by 2027, up from $12.3 billion in 2023.

I’m a finance professor who studies household finance and how people use AI to make money decisions. And I believe the most consequential AI security story right now isn’t unfolding in server rooms, but at kitchen tables.

Old cons, new machinery

None of these scams are new. What AI changed is the cost and the quality of the cons.

Cloning a voice now takes a few seconds of audio and cheap consumer tools. In one study, listeners were able to identify an AI-generated voice only about 60% of the time. Video is heading the same way. In 2024, a finance employee at the architecture and design firm Arup was tricked into wiring about $25 million to fraudsters after they set up a video meeting “staffed” by deepfakes of the chief financial officer and several colleagues.

Phishing has improved, too. Clumsy wording and odd formatting used to give fraudulent emails away. Language models now write clean, fluent messages and can personalize them at scale using details scraped from social media. Deepfake videos of well-known business figures pitch bogus trading platforms.

The same pressure is visible in business losses. The cyber insurer Resilience reported that more than 85% of the losses in its claims portfolio in the first half of 2026 stemmed from attacks aimed at people rather than systems.

Why careful people fall for it

We’d like to believe that only careless people get taken. Research in behavioral finance says otherwise.

These scams are engineered around fear and urgency: a panicked grandchild, a boss demanding a same-day transfer, an investment window that closes tonight. Stress narrows attention and pushes people toward fast, intuitive judgments at the very moment they need slow, deliberate ones. Fraudsters also strike a pose of authority, whether a CFO’s face or a government agency’s letterhead, because most people defer to it.

Fluency matters as well. My own research examines how the smoothness of AI-generated communication leads people to trust it. A message with no typos, in a voice that sounds exactly right, sails past defenses that a clumsy fake would have tripped.

Nobody plans to make a major financial decision mid-panic. That’s exactly why scammers manufacture the panic.

The quiet version of the attack

AI-enabled theft doesn’t necessarily involve talking to the victim. Stolen personal data sells for a few dollars on dark web markets. Criminals then feed it to automated AI agents that probe bank and fintech systems around the clock, testing credentials and hunting for weak points at a speed no human crew could match. Last fall, the AI company Anthropic disrupted an espionage campaign in which an AI agent performed 80% to 90% of the intrusion work against roughly 30 targets, including financial institutions.

When an attacker gets into a customer account, the takeover can be over in minutes. Instant payment platforms like Zelle, built for speed and convenience, become the getaway car. The money typically moves within minutes, and getting it back is almost impossible.

How to protect you and your loved ones

Banks defend their own wire rooms with procedures, not vigilance. Households can borrow those procedures.

Check by calling back. When you get a suspicious call, hang up and dial a number you already know – like your bank’s fraud hotline – and never one the caller or message supplies. The point is to leave the channel the scammer controls. A cloned voice can’t answer your grandson’s real phone.

Trust and verify. You should agree on a robust family code word for emergencies and treat any request for money that lacks it as fake. Require two people in your household to sign off on any large transfer, so nobody moves serious money alone and under pressure. And build in a delay, such as a self-imposed 24-hour wait, before any big payment. Urgency is the scammer’s tool. Slowness is yours.

Add layers of protection. Protect the accounts themselves, too. Turn on two-factor login for financial accounts, and never share a verification code with someone who contacts you. That code is the second lock on your door, and the only reason a caller wants it is to get in.

You should also switch on your bank’s transaction alerts so a takeover announces itself in minutes, and consider a credit freeze, which is free and blocks thieves from opening new accounts with data bought on the dark web.

Protect older people. With $352 million of reported AI-related losses coming from Americans over 60, conversations with older family members are key. Walk through the callback rule and the code word, and ask their bank or brokerage about adding a trusted contact they can call. It costs nothing, and it gives the institution a way to raise an alarm before the money moves.

If money has already moved, you should call your bank immediately, ask it to attempt a recovery, then report the scam to the Federal Trade Commission.

Where habits end, rules should begin

Good habits raise the cost of every one of these scams, but they can’t do it all. This is where the U.S. regulations have fallen behind the technology.

Federal law is supposed to protect consumers from unauthorized electronic transfers, and regulators have said that a transfer set in motion by a fraudster counts as unauthorized even when the victim was tricked into handing over account credentials.

In practice, though, victims of instant-payment fraud often recover little. Banks frequently classify losses as “authorized” when a customer was deceived into approving the payment, and even obvious victims of such takeovers can face long fights over reimbursement.

For example, the Consumer Financial Protection Bureau sued Zelle’s operator and three of the country’s largest banks over their response to alleged fraud in late 2024, then dropped the case in March 2025. New York’s attorney general has since filed her own lawsuit, which a judge allowed to proceed in July. Zelle’s operator denies the allegations and says it will appeal.

The U.K. has taken a different path. Since late 2024, U.K. banks have been required to reimburse most scam victims up to £85,000, roughly $115,000, with the cost split between the sending and receiving institutions. The logic is that banks are best equipped to fight fraud, since they run security teams and networkwide analytics that can spot suspicious patterns across millions of transactions. What they had lacked was a strong financial reason to deploy them fully, and the reimbursement rule supplied it.

The regulator’s own dashboard shows that 88% of the money lost to eligible scams has been returned to victims since the rules took effect. More telling: An independent evaluation found that scam losses fell by roughly a fifth in the rule’s first year. That shows that when banks bear the losses, they find ways to prevent them.

I believe American regulators and Congress should study that model closely. When payments are instant and irreversible, the risk cannot rest almost entirely on the customer, who is the least-equipped party in the chain.

Pawan Jain, Associate Professor of Finance, University of Michigan Flint

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

Fact-Checked by Irfan Ahmad.

Read next: US Adults Reporting AI Use Six Days a Week More Than Doubled From March to August 2026

by External Contributor via Digital Information World

US Adults Reporting AI Use Six Days a Week More Than Doubled From March to August 2026

By Amreeta Das, Caroline Falkman Olsson, and Yafah Edelman, Epoch AI

Polling by Epoch AI and Ipsos finds the share of US adults who reported using AI at least 6 days in the previous week more than doubled from March to August 2026, rising from 8% to 19%. Over the same period, the share of US adults who reported using AI just one day in the previous week fell from 17% to 10%.

Results are based on two Epoch AI/Ipsos surveys of US adults, fielded March 3–5, 2026 (n=2,017) and August 28–30, 2026 (n=1,016). Respondents were recruited at random, and estimates are weighted to be representative of US adults.

Fact-Checked by Irfan Ahmad.

Read next: Big AI wants to slow down AI research. Is it a safety pause or a strategic retreat?
by External Contributor via Digital Information World

Big AI wants to slow down AI research. Is it a safety pause or a strategic retreat?

Andrew Cullen, The University of Melbourne

Image: Jose Castillo - Unsplash

Over the weekend, Anthropic chief executive Dario Amodei called for artificial intelligence (AI) companies, including his own, to slow down their work. Sam Altman and Elon Musk, heads of rivals OpenAI and xAI respectively, agreed.

The move reflects concern across the AI industry and more broadly about the dangers of new, rapidly improving systems. Recent high-profile incidents such as OpenAI AI agents hacking another company and hijacking a public website have shown current systems can break out of safety confines – and even more capable systems are in development.

Further complicating AI safety is the tension between safety and performance. Companies will be reluctant to limit the performance of their models in the name of safety for fear of losing ground to competitors. US President Donald Trump has also rejected calls for a slowdown, for fear of losing ground to China.

This means any successful effort at “pacing the rate of capabilities advancement so that risk prevention has time to keep up”, as Amodei puts it, will require significant cooperation between rival companies – and nations.

Risk minimisation

New technologies often bring new risks, and new concerns. Often governments, researchers and companies do find ways to manage those risks.

In 1975, the Asilomar conference on then-new DNA technologies did much to ensure research didn’t get ahead of our understanding of safety and risk. Similarly, in the 1990s, the US government attempted to build a consensus on limiting cryptography and computer security.

The AI situation has an extra twist: the industry is in the middle of a gold rush. Scientific rivals may be able to restrain themselves, but commercial rivals rarely hold back.

There are clear precedents for self-regulation failing in the face of competitive pressure. In the Boeing 737 Max disaster in 2018, for example, pressure to catch up to rival Airbus led Boeing to hide the limitations of the Max, which ultimately cost lives.

In the AI race, the scale of the competitive tension is even greater. Anthropic and OpenAI are both competing to establish market dominance before pursuing share market listings that could raise tens or hundreds of billions of dollars.

And at the nation-state level, the stakes are higher again, with the US and China each hoping to use the new technology for geopolitical advantage.

The politics of a pause

Amodei’s slowdown proposal centres around a three-step plan: embedding independent third-party safety reviewers, establishing coordinated industry safety standards within democratic nations, and eventually securing global agreements. This would include strict limits on AI chip exports to companies and countries that do not agree to prioritise AI safety.

Anthropic and OpenAI have already agreed to the first phase of this plan, despite their history of suing, publicly insulting, and undercutting each other in pursuit of market dominance.

While recent high-profile hacks may have forced their hands, the leading AI companies may benefit from a development pause or slowdown. For one thing, it could put off strict legislation such as US senator Bernie Sanders’ proposed Ban Artificial Superintelligence Act. For another, by creating expensive safety standards and limiting chip exports, it could block smaller competitors – especially Chinese companies such as DeepSeek and Alibaba – from catching up.

A pause would also give the overstretched frontier labs a chance to recoup, recover, and focus on profits over progress.

These AI labs have spent enormous amounts to produce their existing models, which must be repaid. At the same time, progress is hitting speedbumps as new high-quality training data gets harder to find, and building the massive data centre infrastructure needed to sustain development is a challenge in itself.

A coordinated safety pause could be a convenient public reason for a plateau in AI model performance.

What can be done

Making AI safe won’t be easy. The governance of software is notoriously difficult, as past attempts to legislate encryption software have shown.

However, unlike other software, AI is dependent on relatively scarce physical hardware. It needs advanced silicon chips and the massive data centres required to power them.

This is where governments have real leverage. Here they have ways to monitor and control AI development, if they can find the legislative will.

In the meantime, there are steps businesses and governments can take to minimise how exposed we all are to AI-driven harms. This should include ensuring that critical safety infrastructure – like power grids, water supplies, and military systems – are not only isolated from AI, but potentially even isolated entirely from the internet.

And the question of liability is also important. It can’t just be the users of AI systems who face legal jeopardy for acts assisted by AI, but also the people responsible for making AI systems.

Fundamentally, harm produced by AI – even by “autonomous” AI systems – isn’t an abstract technological byproduct. It is the direct result of decisions made by both those making AI, and those using it.

Andrew Cullen, Senior Research Fellow, School of Computing and Information Systems, The University of Melbourne

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

Fact-Checked by Irfan Ahmad.

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