#FactCheck:Old 9/11 Videos Falsely Linked to Iran’s Attack on Israel Amid Ongoing Conflict

Introduction

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You ask an app for directions to a street you've driven down a hundred times. You let autocomplete finish your sentence before you've decided what you meant to say. You take a photo of a document instead of reading it, trusting the summary a model hands back. None of these moments feel like a loss. Each one is, on its own, a reasonable trade of effort for convenience. But add them up across a year, a career, an industry, and you start to wonder what exactly we've been trading away. Every technology wave produces its own founding myth. For AI, the myth is that intelligence can be manufactured at scale, bottled into a model, and dispensed on demand - cheaper, faster, and eventually better than the human original. It's a seductive story, and one we've been telling ourselves so uncritically that we've stopped noticing what it costs.

The casualties of this bet are rarely dramatic. Nobody announces that a skill has quietly atrophied, or that a habit of independent judgement has gone unused long enough to weaken. These losses don't show up as headlines; they show up later, as gaps, when the system that was supposed to think for us turns out not to have been thinking at all. Ford Motor Company's recent decision to rehire around 350 veteran engineers, after leaning heavily on AI-driven quality systems, is a small but telling data point.¹ The lesson isn't that automation failed outright — it's that a process can be automated without the judgement that made the process work ever being captured in the first place. That distinction between automating a task and actually preserving the human expertise behind it is the real subject of this AI moment.

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How Organisations Are Using AI in Decision-Making

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More organisations are now leaning on AI not just to execute tasks, but to help shape decisions. Deloitte's 2026 Global Human Capital Trends survey found that 60% of executives now regularly use AI to support their decisions, and the same report cites Gartner's projection that by 2027, half of all business decisions will be augmented or automated by AI agents. Companies like Netflix and Amazon are often pointed to as examples of this working well using AI to enhance recommendations and logistics while keeping people involved in the interpretation, generating significant value in the process. Elsewhere, results have been more mixed: MIT's "State of AI in Business 2025" study found that 95% of generative AI pilots showed no measurable P&L impact within six months, often because this initiative failed to integrate feedback or adapt to context rather than because the underlying model was flawed. Critics have noted the study used a narrow definition of success (six-month, bottom-line ROI), so the figure may understate the value AI creates in ways that aren't captured on the P&L. Notably, this is not an argument against using AI. It is an argument about how we use it and why the human-in-the-loop principle, keeping people actively involved in judgement rather than passively rubber-stamping outputs, is not a compliance checkbox but the thing that determines whether automation actually works. That distinction, between automating a task and preserving the human expertise behind it, is the point of contention.

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Finding the Balance

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The lesson isn't to use AI less, it's to be deliberate about where it sits in the process. The strongest results come from pairing AI's speed with human judgement, not swapping one for the other. That means keeping a clear owner for important decisions, checking that the model is optimising for the right goal, and treating its output as a strong first draft rather than a final answer. Used this way, AI doesn't replace thinking, it gives good judgement more room to work.

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Two Framings We Should Retire

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Conversations about AI adoption keep falling into two lazy framings. The first is AI versus humans, as if technology and workforce are locked in a zero-sum contest for relevance. The second is AI versus jobs, reducing every discussion to headcount and displacement. Both are legitimate concerns, but they crowd out a more urgent question: as AI gets embedded deeper into how decisions are made, what happens to the quality of the decisions themselves? This is not a question about whether AI is useful and it plainly is. It is a question about what gets quietly outsourced along with the task, and whether anyone notices before it matters.

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Why “Wisdom of Crowds” Does Not Automatically Apply to AI

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A comforting analogy often gets reached for: surely, with millions of people using the same models, errors will average out, the way independent forecasters tend to converge on accurate estimates.² That analogy breaks down where it matters most. The wisdom-of-crowds effect depends on independent thinking, genuinely diverse information, and an aggregation mechanism that does not distort the signal. When millions of people query the same underlying model, those conditions collapse. Everyone draws from the same statistical engine, trained on overlapping data, tuned toward similar “safe” answers. The apparent agreement is not corroboration, it is an echo. This creates a genuinely new risk: AI can be confidently, fluently, and uniformly wrong across an entire organisation at once, without the friction that would normally surface an error in a single person's judgement.

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The Casualties, Named Plainly

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Several things erode quietly when organisations are not deliberate about integrating AI into decisions. Independent judgement is the first casualty of the willingness to form a view before checking what the model says. Verification effort follows: generative AI collapses retrieval and generation into one fluent output, and people invest less effort checking something that already sounds complete and well-reasoned. Diversity of thought narrows as more decision-makers lean on the same handful of models for research and drafting, quietly reducing the range of framings available when it matters most. Accountability becomes harder to trace when a recommendation generated by a model and passed along with minimal scrutiny creates a strange vacuum where a decision was made but nobody quite owns it. And informational anchoring sets in, where a signal becomes a coordination point simply because everyone is looking at it, regardless of its accuracy.

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Why Human-in-the-Loop Is a Design Requirement

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“Human in the loop” often becomes a rubber-stamp step rather than genuine scrutiny. That is a mistake, because the functions humans provide are structural, not decorative. Context that a model cannot infer  history, relationships, unstated constraints  shapes whether a reasonable-sounding answer is right in a specific situation. Domain expertise built over years lets someone recognise when a fluent answer is subtly wrong. Ethical judgement decides trade-offs a model has no standing to make on an organisation's behalf. Accountability means someone can be asked why a decision was made and answer from reasoning, not from “the system recommended it.” And the rarest function of all is the willingness to challenge a convincing answer and resisting the very fluency that makes AI output persuasive.

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What This Looks Like in Practice

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For organisations, the goal is not slowing AI adoption but being deliberate about where human judgement stays load-bearing. AI output should default to draft status until a qualified person has actively tested its logic against context the model lacks. Teams using the same AI tools for analysis should build in a step that actively seeks disagreement, rather than assuming convergence means correctness. Ford's decision to bring engineers back to lead design reviews is instructive: expertise, once encoded into a system, is not safe to let atrophy in the people who built it.³ Verification should be visible and required for decisions with real financial, legal, safety, or reputational consequences. And organisations should track which decisions were AI-assisted and who owned the final call, so accountability stays traceable rather than quietly disappearing.

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Conclusion

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Decades ago, management thinkers warned that automating a broken process only helps an organisation fail faster. The AI era raises the stakes on that warning: judgement itself, the hard-won capacity to reason well under uncertainty, can be automated away without anyone deciding to give it up. Machines already process information faster than any team of people. What they cannot yet do is originate the wisdom that comes from human experience, accountability, and the willingness to be told one is wrong. That capacity erodes not because AI is powerful, but because organisations stop deliberately exercising it. The real task ahead is not resisting AI, but ensuring that as it takes on more of the work of deciding, humans deliberately keep hold of the responsibility of deciding.

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References

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1. https://www.assemblymag.com/articles/100186-ford-rehires-veteran-engineers-to-improve-ai-vehicle-quality
2. https://finance.yahoo.com/technology/ai/articles/ford-rehires-veteran-engineers-ai-144332497.html
3. https://finance.yahoo.com/technology/ai/articles/ford-rehires-more-300-engineers-162210705.html
4. https://www.msn.com/en-us/money/other/ford-rehires-hundreds-of-engineers-after-ai-struggles-to-improve-quality/ar-AA26P4QB?ocid=BingNewsSerp
5. https://www.foxbusiness.com/technology/ford-rehires-experienced-engineers-after-ai-misses-mark
6. https://www.livemint.com/opinion/online-views/artificial-wisdom-of-crowds-jobs-crisis-ai-technology-automation-openai-model-11785009289768.html
7. https://www.deloitte.com/us/en/insights/topics/talent/human-capital-trends/2026/decision-making-with-ai.html
8. https://www.hpcwire.com/aiwire/2026/03/04/deloittes-state-of-ai-2026-why-enterprise-execution-is-falling-behind-adoption/ and Legal.io summary: https://www.legal.io/blog/5719519/MIT-Report-Finds-95-of-AI-Pilots-Fail-to-Deliver-ROI-Exposing-GenAI-Divide
9. https://www.marketingaiinstitute.com/blog/mit-study-ai-pilots

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Introduction

The term ‘super spreader’ is used to refer to social media and digital platform accounts that are able to quickly transmit information to a significantly large audience base in a short duration. The analogy references the medical term, where a small group of individuals is able to rapidly amplify the spread of an infection across a huge population. The fact that a few handful accounts are able to impact and influence many is attributed to a number of factors like large follower bases, high engagement rates, content attractiveness or virality and perceived credibility.

Super spreader accounts have become a considerable threat on social media because they are responsible for generating a large amount of low-credibility material online. These individuals or groups may create or disseminate low-credibility content for a number of reasons, running from social media fame to garnering political influence, from intentionally spreading propaganda to seeking financial gains. Given the exponential reach of these accounts, identifying, tracing and categorising such accounts as the sources of misinformation can be tricky. It can be equally difficult to actually recognise the content they spread for the misinformation that it actually is. 

How Do A Few Accounts Spark Widespread Misinformation? 

Recent research suggests that misinformation superspreaders, who consistently distribute low-credibility content, may be the primary cause of the issue of widespread misinformation about different topics. A study[1] by a team of social media analysts at Indiana University has found that a significant portion of tweets spreading misinformation are sent by a small percentage of a given user base. The researchers conducted a review of 2,397,388 tweets posted on Twitter (now X) that were flagged as having low credibility and details on who was sending them. The study found that it does not take a lot of influencers to sway the beliefs and opinions of large numbers. This is attributed to the impact of what they describe as superspreaders. The researchers collected 10 months of data, which added up to 2,397,388 tweets sent by 448,103 users, and then reviewed it, looking for tweets that were flagged as containing low-credibility information. They found that approximately a third of the low-credibility tweets had been posted by people using just 10 accounts, and that just 1,000 accounts were responsible for posting approximately 70% of such tweets.[2]

Case Study

1. How Misinformation ‘Superspreaders’ Seed False Election Theories

During the 2020 U.S. presidential election, a small group of "repeat spreaders" aggressively pushed false election claims across various social media platforms for political gain, and this even led to rallies and radicalisation in the U.S.[3] Superspreaders accounts were responsible for disseminating a disproportionately large amount of misinformation related to the election, influencing public opinion and potentially undermining the electoral process.

In the domestic context, India was ranked highest for the risk of misinformation and disinformation according to experts surveyed for the World Economic Forum’s 2024 Global Risk Report. In today's digital age, misinformation, deep fakes, and AI-generated fakes pose a significant threat to the integrity of elections and democratic processes worldwide. With 64 countries conducting elections in 2024, the dissemination of false information carries grave implications that could influence outcomes and shape long-term socio-political landscapes. During the 2024 Indian elections, we witnessed a notable surge in deepfake videos of political personalities, raising concerns about the influence of misinformation on election outcomes.

2. Role of Superspreaders During Covid-19

Clarity in public health communication is important when any grey areas or gaps in information can be manipulated so quickly. During the COVID-19 pandemic, misinformation related to the virus, vaccines, and public health measures spread rapidly on social media platforms, including Twitter (Now X). Some prominent accounts or popular pages on platforms like Facebook and Twitter(now X) were identified as superspreaders of COVID-19 misinformation, contributing to public confusion and potentially hindering efforts to combat the pandemic.

As per the Center for Countering Digital Hate Inc (US), The "disinformation dozen," a group of 12 prominent anti-vaccine accounts[4], were found to be responsible for a large amount of anti-vaccine content circulating on social media platforms, highlighting the significant role of superspreaders in influencing public perceptions and behaviours during a health crisis.

There are also incidents where users are unknowingly engaged in spreading misinformation by forwarding information or content which are not always shared by the original source but often just propagated by amplifiers, using other sources, websites, or YouTube videos that help in dissemination. The intermediary sharers amplify these messages on their pages, which is where it takes off. Hence such users do not always have to be the ones creating or deliberately popularising the misinformation, but they are the ones who expose more people to it because of their broad reach. This was observed during the pandemic when a handful of people were able to create a heavy digital impact sharing vaccine/virus-related misinformation. 

3. Role of Superspreaders in Influencing Investments and Finance

Misinformation and rumours in finance may have a considerable influence on stock markets, investor behaviour, and national financial stability. Individuals or accounts with huge followings or influence in the financial niche can operate as superspreaders of erroneous information, potentially leading to market manipulation, panic selling, or incorrect impressions about individual firms or investments.

Superspreaders in the finance domain can cause volatility in markets, affect investor confidence, and even trigger regulatory responses to address the spread of false information that may harm market integrity. In fact, there has been a rise in deepfake videos, and fake endorsements, with multiple social media profiles providing unsanctioned investing advice and directing followers to particular channels. This leads investors into dangerous financial decisions. The issue intensifies when scammers employ deepfake videos of notable personalities to boost their reputation and can actually shape people’s financial decisions. 

Bots and Misinformation Spread on Social Media

Bots are automated accounts that are designed to execute certain activities, such as liking, sharing, or retweeting material, and they can broaden the reach of misinformation by swiftly spreading false narratives and adding to the virality of a certain piece of content. They can also artificially boost the popularity of disinformation by posting phony likes, shares, and comments, making it look more genuine and trustworthy to unsuspecting users. Bots can exploit social network algorithms by establishing false identities that interact with one another and with real users, increasing the spread of disinformation and pushing it to the top of users' feeds and search results.

Bots can use current topics or hashtags to introduce misinformation into popular conversations, allowing misleading information to acquire traction and reach a broader audience. They can lead to the construction of echo chambers, in which users are exposed to a narrow variety of perspectives and information, exacerbating the spread of disinformation inside restricted online groups. There are incidents reported where bot's were found as the sharers of content from low-credibility sources.

Bots are frequently employed as part of planned misinformation campaigns designed to propagate false information for political, ideological, or commercial gain. Bots, by automating the distribution of misleading information, can make it impossible to trace the misinformation back to its source. Understanding how bots work and their influence on information ecosystems is critical for combatting disinformation and increasing digital literacy among social media users.

CyberPeace Policy Recommendations

1. Recommendations/Advisory for Netizens:

1. Educating oneself: Netizens need to stay informed about current events, reliable fact-checking sources, misinformation counter-strategies, and common misinformation tactics, so that they can verify potentially problematic content before sharing.
2. Recognising the threats and vulnerabilities: It is important for netizens to understand the consequences of spreading or consuming inaccurate information, fake news, or misinformation. Netizens must be cautious of sensationalised content spreading on social media as it might attempt to provoke strong reactions or to mold public opinions. Netizens must consider questioning the credibility of information, verifying its sources, and developing cognitive skills to identify low-credibility content and counter misinformation.
3. Practice caution and skepticism: Netizens are advised to develop a healthy skepticism towards online information, and critically analyse the veracity of all information sources. Before spreading any strong opinions or claims, one must seek supporting evidence,  factual data, and expert opinions, and verify and validate claims with reliable sources or fact-checking entities.
4. Good netiquette on the Internet, thinking before forwarding any information: It is important for netizens to practice good netiquette in the online information landscape. One must exercise caution while sharing any information, especially if the information seems incorrect, unverified or controversial. It's important to critically examine facts and recognise and understand the implications of sharing false, manipulative, misleading or fake information/content. Netizens must also promote critical thinking and encourage their loved ones to think critically, verify information, seek reliable sources and counter misinformation.
5. Adopting and promoting Prebunking and Debunking strategies: Prebunking and debunking are two effective strategies to counter misinformation. Netizens are advised to engage in sharing only accurate information and do fact-checking to debunk any misinformation. They can rely on reputable fact-checking experts/entities who are regularly engaged in producing prebunking and debunking reports and material. Netizens are further advised to familiarise themselves with fact-checking websites, and resources and verify the information.

2. Recommendations for tech/social media platforms 

1. Detect, report and block malicious accounts: Tech/social media platforms must implement strict user authentication mechanisms to verify account holders' identities to minimise the formation of fraudulent or malicious accounts. This is imperative to weed out suspicious social media accounts, misinformation superspreader accounts and bots accounts. Platforms must be capable of analysing public content, especially viral or suspicious content to ascertain whether it is misleading, AI-generated, fake or deliberately misleading. Upon detection, platform operators must block malicious/ superspreader accounts. The same approach must apply to other community guidelines’ violations as well. 
2. Algorithm Improvements: Tech/social media platform operators must develop and deploy advanced algorithm mechanisms to detect suspicious accounts and recognise repetitive posting of misinformation. They can utilise advanced algorithms to identify such patterns and flag any misleading, inaccurate, or fake information.
3. Dedicated Reporting Tools: It is important for the tech/social media platforms to adopt robust policies to take action against social media accounts engaged in malicious activities such as spreading misinformation, disinformation, and propaganda. They must empower users on the platforms to flag/report suspicious accounts, and misleading content or misinformation through user-friendly reporting tools.
4. Holistic Approach: The battle against online mis/disinformation necessitates a thorough examination of the processes through which it spreads. This involves investing in information literacy education, modifying algorithms to provide exposure to varied viewpoints, and working on detecting malevolent bots that spread misleading information. Social media sites can employ similar algorithms internally to eliminate accounts that appear to be bots. All stakeholders must encourage digital literacy efforts that enable consumers to critically analyse information, verify sources, and report suspect content. Implementing prebunking and debunking strategies. These efforts can be further supported by collaboration with relevant entities such as cybersecurity experts, fact-checking entities, researchers, policy analysts and the government to combat the misinformation warfare on the Internet.  

References:

1. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0302201 {1}
2. https://phys.org/news/2024-05-superspreaders-responsible-large-portion-misinformation.html#google_vignette {2}
3. https://phys.org/news/2024-05-superspreaders-responsible-large-portion-misinformation.html#google_vignette {3}
4. https://counterhate.com/research/the-disinformation-dozen/ {4}
5. https://phys.org/news/2024-05-superspreaders-responsible-large-portion-misinformation.html#google_vignette
6. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0302201
7. https://www.nytimes.com/2020/11/23/technology/election-misinformation-facebook-twitter.html
8. https://www.wbur.org/onpoint/2021/08/06/vaccine-misinformation-and-a-look-inside-the-disinformation-dozen
9. https://healthfeedback.org/misinformation-superspreaders-thriving-on-musk-owned-twitter/
10. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139392/
11. https://www.jmir.org/2021/5/e26933/
12. https://www.yahoo.com/news/7-ways-avoid-becoming-misinformation-121939834.html

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BharOS’s successful testing grabbed massive online attention after Ashwini Vaishnaw, Minister of Communications and Electronics & IT, and Union Education Minister Dharmendra Pradhan unveiled the new mobile operating system. On Data Privacy Day, January 28, it’s appropriate to discuss the safety factors.

The OS is developed by JandKops, which has been incubated by IIT Madras Pravartak Technologies Foundation. It is claimed that BharOS will ensure the prevention of the “execution of any malware” and “execution of any malicious application”.

Even though it is called a Made in India OS, there are many people who disagree with this. It is because the OS is based on an AOSP (Android Open Source Project). It includes similar methodologies, functionalities, and basics used in Google Android.

Global safety factor

Security and data safety has been worldwide issue. A few years ago, Alphabet CEO Sundar Pichai also testified in front of US Congress while facing questions related to privacy, data collection, and location tracking.

While experts say that Android’s app ecosystem is a privacy and security disaster, a study that examined 82,501 apps pre-installed on 1,742 Android smartphones sold by 214 vendors concluded that users are woefully unaware of the significant security and privacy risks posed by pre-installed applications.

Even Apple, which takes cybersafety issues as a top priority, sometimes finds itself in a vulnerable situation. For example, last year Apple users were advised to update their devices to protect against a pair of security flaws that could allow attackers to take complete control.

It was said that one of the software flaws affected the kernel, the deepest layer of the OS shared by all Apple devices, while the other had an impact on WebKit, the technology that powers the Safari web browser.

Security researchers, including NordVPN, said that Apple’s closed development OS makes it more difficult for hackers to develop exploits, while Android raises the threat level since anyone can see its source code to develop exploits.

BharOS is not like iOS but it is kind of similar to Android and based on AOSP. So the question is, how safe would this OS be?

‘Security blanket’

Sandip Kumar Panda, Co-founder and CEO of InstaSafe, told News18: “BharOS acts as a security blanket for devices. The framework is designed in a manner that it prevents the execution of any malicious app and verifies each app on the devices before making it live on the BharOS platform.”

There are no apps without any vulnerabilities, he said. “As the app development progresses, vulnerabilities get introduced either in the form of insecure coding practices or third-party software vulnerabilities integrated with the platform. Since several Android vulnerabilities were discovered over the years, all those bugs would have been fixed now and updates would already have been for AOSP, which will be much more mature now,” he added.

Vineet Kumar, Founder and President of CyberPeace Foundation, believes that “the use of AOSP as the foundation for BharOS is a positive step” as it is a robust platform.

But according to him, it is important to note that no OS can be completely immune to all forms of cyber threats. “The key to staying safe online is to stay vigilant, use security software, keep your software updated, and be mindful of the apps you install and the websites you visit,” he said,

Furthermore, the expert stated that it is possible to make an OS more secure by implementing a variety of security features and technologies such as sandboxing, whitelisting, and application control, as well as rigorous testing and code review processes.

Kumar said: “It would be important for an independent, reputable security firm to evaluate BharOS and test its security features before it can be stated with certainty that it is more secure than other OSs.”

It is difficult to say whether the BharOS will be free of cybersecurity issues without more information about the specific features and security measures that have been implemented, he noted while adding that this OS has to go through a rigorous testing and certification process.

“It will be important to see how it measures up against established security standards and how well it can withstand real-world attacks,” the expert stated.

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Reference Link : https://www.news18.com/amp/news/tech/data-privacy-day-how-safe-is-bharos-what-do-cybersecurity-experts-say-you-are-about-to-find-out-6932521.html