Oversight Board Says Meta's AI Deepfake Policies Are Inadequate

Fact-Checked by Irfan Ahmad

Meta's Oversight Board said Sept. 17 that its policies on AI-manipulated imagery and deepfake labeling are inadequate in two cases involving Facebook posts.

Image: Zulfugar Karimov - Unsplash

In one case, the Board ordered removal of an AI-generated video targeting a young Muslim woman in Europe. A majority found Meta's definition of "unwanted manipulated imagery" too narrow because it does not cover falsifying a private person's words and actions. The Board said, "Meta also defines mass harassment campaigns too narrowly, according to the majority, and the company should adopt new measures to reduce the burden on victims reporting them."

In a separate case, the Board ordered removal of an AI-generated video that falsely portrayed a Scottish Labour councillor making a statement about refugees. The Board found that the video violated Meta's Hateful Conduct policy. A majority also found Meta's rules for labeling deepfakes inadequate and said the video should have received a "High Risk AI" label.

The Board recommended expanding the situations when "high risk" labels can be applied, taking more measures to reduce the spread of deceptive AI content, increasing penalties for accounts that repeatedly share it, and providing more transparency on data about when AI labels are applied.

Editor's Note: This post was mistakenly published under the External Contributor category. It was intended to be published under DIW AI Analysis.

Read next:

• AI Is Producing More Software. Why Isn’t It Being Used?

• “Almost Every Job Has Tasks That AI Can Change”. Economist Erik Brynjolfsson says AI’s transformation of work is just beginning and our institutions aren’t ready for it
by External Contributor via Digital Information World

AI Is Producing More Software. Why Isn’t It Being Used?

By Seb Murray

A Wharton study finds that while AI dramatically speeds up software development, human bottlenecks prevent many of those gains from reaching customers.

Image: Joy Christian - Unsplash

Artificial intelligence coding tools dramatically increase developers’ productivity, but much of those gains are lost before they translate into finished software because human bottlenecks persist later in the production process.

That’s according to a new study written by Leon Musolff, a Wharton professor of business economics and public policy, and MIT researchers Mert Demirer and Liyuan Yang. (Editor’s note: This paper was updated with new data after this article was written.)

When looking at the impact of AI tools on coding activity, the gains grew sharply with each new generation of AI tools. Autocomplete systems that suggest the next line of code increased coding activity by 40%. Adding “sync agents,” which edit code alongside developers in real time, lifted the cumulative increase to 140%; “async agents,” which work autonomously from a prompt, pushed it to 180%.

Yet even the biggest cumulative gain translated into only a 50% increase in software projects, and a 30% increase in software releases. “In software, the binding constraint appears to be shifting from writing code to reviewing, integrating, and ultimately distributing it,” wrote the authors in the paper.

What’s Blocking AI Productivity Gains?

The researchers tracked more than 100,000 developers on GitHub, the world’s biggest software development platform, comparing their productivity before and after they adopted the three successive generations of AI coding tools, from 2022 to 2026. They combined those public GitHub records with Microsoft data on developers’ use of the tools to identify when they first adopted the technology.

The findings suggest that AI can dramatically speed up individual coding tasks, but those gains will not automatically translate into more finished software — unless AI can also automate more of the work involved in reviewing, integrating, and releasing software.

Increasingly powerful AI coding tools have made it possible to generate working software from simple prompts, dramatically lowering the barriers to software development. That has helped fuel the recent “vibe coding” boom, allowing employees with limited programming experience to build applications in minutes. But the research suggests writing code is no longer the main block.

“If the world froze at today’s level of AI capabilities, these results would be a bit of a cold shower.” — Leon Musolff

Will AI Coding Tools Improve?

So what lessons should companies draw from the findings? “If the world froze at today’s level of AI capabilities, these results would be a bit of a cold shower,” Musolff said.

However, the tools are improving apace. “We studied these tools in a previous paper, and it’s night and day,” said Musolff. “A 30% increase in software releases — there are very few technologies you can invest in today that deliver those kinds of gains.”

The researchers also found that each new generation of tools is tackling a later stage of the software development process, so the gap between gains in coding productivity and gains in finished software could begin to narrow as the AI gets better.

The paper says that if the tech can produce higher-quality code that requires less human review, today’s bottlenecks may prove temporary.

Some tech companies are already trying to tackle that problem by developing AI tools that review machine-written code. But Musolff is unconvinced they can yet match human judgement.

“If the same AI that wrote the code also reviews it, that doesn’t really solve the problem. The review just isn’t of the same quality,” he said.

“If the same AI that wrote the code also reviews it, that doesn’t really solve the problem.” — Leon Musolff

Is User Adoption the Next Barrier?

Even once software is released, it still has to find traction with an audience. The research found AI is increasing the number of new software applications, but not user adoption.

The researchers studied the four biggest software marketplaces — Apple App Store, Google Play Store, Chrome Web Store and SourceForge — and found a broad surge in new software applications since mid-2025. But crucially, no increase in overall usage.

On Apple’s App Store, for example, monthly new releases rose from around 30,000 before AI coding agents arrived in early 2025 to roughly 100,000 per month by April 2026. Yet total usage remained flat or declined across the four major app stores.

“It could simply be that it’s much harder to discover new applications when there’s such a flood of them,” said Musolff. “Alternatively, even once you’ve shipped an app, there’s another skill involved: iterating with users.”

In other words: Getting software into users’ hands is only the start.

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©2026 Knowledge at Wharton. Originally published by Wharton School, University of Pennsylvania on September 8, 2026. Republished on Digital Information World with permission.

Fact-Checked by Irfan Ahmad.

Read next: “Almost Every Job Has Tasks That AI Can Change”. Economist Erik Brynjolfsson says AI’s transformation of work is just beginning and our institutions aren’t ready for it
by External Contributor via Digital Information World

FBI Warns of AI-Assisted Scams Impersonating Government Officials as AI Fraud Shows Higher Profitability

The FBI warned Sept. 17 that scammers are impersonating law enforcement and government officials to extort money or personally identifiable information from victims.

Image: Growtika - Unsplash

The agency's Internet Crime Complaint Center (IC3) received nearly 61,000 complaints of law enforcement or government impersonation scams between January 2025 and July 2026, with losses exceeding $1.6 billion.

Scammers primarily use unsolicited phone calls, but also texts and emails. They may spoof phone numbers, email addresses and employee names. The FBI said scammers can accuse victims of crimes, missed jury duty or court dates, or expiring licenses while threatening arrest, fine, prosecution or other consequences.

The FBI said scammers use artificial intelligence (AI) to appear as law enforcement and government officials during video calls, adding the appearance of legitimacy.

The FBI's warning about AI-assisted impersonation comes as Interpol has reported a financial advantage for AI-enabled fraud. The Financial Times reported Sept. 14 that Interpol recently found AI-enabled fraud was 4.5 times more profitable than traditional fraud.

The FBI said government and law enforcement authorities will never contact the public by phone or text to demand payment or request personal or sensitive information. They also "will never request payment via prepaid cards, cryptocurrency, or courier." 

The FBI said, verify callers and their credentials using official contact information, and carefully check URLs before clicking to ensure they match the agency's official domain.

Fact-Checked by Irfan Ahmad.

Read next:

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• How advertising turns our insecurities into profit — and how you can resist the manipulation
by AI Analysis via Digital Information World

Advertising is coming to AI chatbots — and it could influence the answers you get

By Carlos Diaz Ruiz, Hanken School of Economics; Broderick Turner, Virginia Tech; Erick M. Mas, University of South Florida, and Zeynep Arsel, Concordia University

Image: Solen Feyissa - Unsplash

When Google founders Sergey Brin and Larry Page were still university researchers, they warned that advertising could undermine the trustworthiness of search engines. In their landmark paper presenting Google, they also wrote:

“The goals of the advertising business model do not always correspond to providing quality search to users […] we expect that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumer.”

More than two decades later, a similar question is emerging around artificial intelligence. As companies race to commercialize AI systems to offset the amount of cash they are burning through, advertising is becoming an increasingly attractive source of revenue.

But advertising within AI systems presents a new challenge. Unlike online ads, which generally appear alongside information as banners or sponsored links, AI advertising is being integrated into the conversation itself, potentially changing the answers you receive.

AI’s advertising model

Today, around half of consumers report using AI conversations for search. Indeed, conversations with AI are becoming so common that some even develop dependencies with AI companions. As advertising merges with AI, it could reshape these conversations.

OpenAI has introduced ChatGPT ads designed around the way people interact with AI assistants, while Google is testing sponsored answers within Gemini conversations. Both approaches appear to follow closely how advertising technologies currently work, including real-time bidding and micro-targeting users, which allow advertisers to reach individual users based on behavioural data.

Organizational theorist Henry Chesbrough has argued that technology by itself has no inherent value; that value only arises when it is commercialized through a business model. His insight suggests that emerging technologies can only be examined as commercial systems shaped by financial incentives.

AI companies say advertising will not change answers. Yet advertising technology is difficult to audit because outsiders often lack access to the data, and AdTech companies weaponize complexity to prevent oversight.

Preventing paid influence from shaping AI-generated answers remains largely voluntary and depends on AI companies honouring their promises.

Some AI providers have already raised concerns about the impact of advertising on trust. Perplexity, an AI-powered answer engine, tested sponsored conversations but discontinued the program.

Even with clear labels, users may struggle to distinguish advertising from organically generated information when both appear within an AI conversation.

New risks in the zero-click internet

What happens when advertising no longer directs traffic to a website, but instead becomes part of a monetized AI conversation?

Commercial AI systems are heading towards a “zero-click internet” in which users make purchases within a corporate-controlled AI environment, not independent websites.

This represents a new form of e-commerce where advertising and transactions are integrated directly into AI chats. As a result, valuable user attention and commercial opportunities remain under the control of AI providers.

Our research explores the implications of merging advertising into AI environments. It identifies risks for free speech and free markets. AI-generated answers compress competing perspectives and may make alternative viewpoints less visible. This could create challenges for minority voices and dissenting perspectives.

It also raises questions about whether large corporations could purchase preferential visibility within AI systems. Smaller firms may face pay-to-play barriers, potentially increasing market concentration.

AI and disinformation

Another concern is the amplification of disinformation through AI. The United Nations warns that embedding micro-targeted adverts into AI-generated content could amplify misinformation and polarizing material.

We know that AI answers can be steered by their controlling corporations. One example is SpaceXAI’s Grok chatbot, which generated false “white genocide” claims in response to unrelated prompts and produced other extremist content reportedly due to an unauthorized code change an employee had made.

Merging advertising with AI adds another layer of risk. Existing digital advertising technologies allow messages to be targeted at specific users based on their personal data.

If similar techniques are integrated into AI conversations, misleading or manipulative messages could be delivered privately, without the public scrutiny that accompanies traditional political advertising.

This is one concern associated with “dark money” in AdTech: when third-party entities use digital advertising technologies for illegitimate purposes, such as election interference.

Why current defences fail

Current defences against disinformation were not designed for AI conversations.

Some of the most advanced legislation to counter disinformation, such as the European Union’s Digital Services Act, was designed for social media and search engines, not chatbots. ChatGPT falls under the act’s scope if it works as a search engine. However, the act’s rules do not guarantee the accuracy of each chat.

The principle responses to disinformation, namely fact-checking and media literacy, were developed in an information environment where misleading claims were public and persistent.

Fact-checkers assess claims with public relevance, such as statements from politicians or hoaxes circulating widely on social media. AI conversations, by contrast, are personalized and ephemeral, meaning misleading claims made to individual users may never enter the public information environment where they can be examined.

Imagine a chatbot repeating a fabricated allegation about a candidate in a private conversation with a voter. The practical challenge is that a personalized falsehood may remain invisible to fact-checkers, leaving the voter misled.

Media literacy faces similar limitations. Users are encouraged to examine sources and compare evidence. However, while search engines presented users with ranked sources, AI systems increasingly provide a single generated response: not simply “an” answer, but “the” answer.

For example, a company may flood the web with fabricated reports. The reports can poison or taint the sources an AI system retrieves. When a consumer asks about the product, the AI turns those planted claims into an authoritative answer, complete with citations.

• Also read: How to Identify and Combat Misinformation in the Digital Age

Setting boundaries

Advertising-funded social media is addictive and often rewards conflict. Because controversy attracts attention, platforms can turn it into engagement metrics that advertisers value. Advertisers are also not limited to commercial brands; anyone willing to pay can attempt to increase their reach.

While policymakers are increasingly focused on the governance of digital platforms, there are still few guidelines for sponsored AI conversations.

Policymakers should establish clear boundaries preventing sponsors from shaping the evidence and substance of AI-generated conversations. A label alone is unlikely to be enough.

AI companies promise that advertising revenue will not influence the substance of their answers. However, such promises may come with a “for now” qualification.

Carlos Diaz Ruiz, Associate Professor of Marketing, Hanken School of Economics; Broderick Turner, Assistant Professor of Marketing, Virginia Tech; Erick M. Mas, Assistant Professor of Marketing, Muma College of Business, University of South Florida, and Zeynep Arsel, Professor, Management, University of Bath and Concordia University Chair in Consumption, Markets, and Society, Concordia University

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

Fact-Checked by Irfan Ahmad.

Read next: Could AI really kill off humanity within the decade? Expert Question and Answer

by External Contributor via Digital Information World

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.

Read next: 

• The consequences of relying on AI for accurate news

• Google’s AI overviews reinforce some conspiracy theories – new research

• Missing Information Can Misinform: Readers Don’t Need False Information to Get the Wrong Idea

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 Explore Whether AI Could Become Conscious In The Future

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by AI Analysis via Digital Information World