Taunting and degrading civilians in armed conflict is a clear violation of international law

Shannon Bosch, Edith Cowan University

Image: Ben Gvir/X

In a video posted by Israeli National Security Minister Itamar Ben-Gvir on Wednesday night, detained activists from dozens of countries are shown kneeling on the ground with their foreheads on the floor and hands zip-tied behind their backs.

Some of the activists, who had been intercepted by Israeli forces on a flotilla in the Mediterranean Sea, are then pushed and dragged by Israeli personnel. Ben-Gvir is seen waving an Israeli flag and taunting them.

The video on his X account had a simple message in English: “Welcome to Israel”.

The video sparked widespread international condemnation. Australian Foreign Minister Penny Wong called it “shocking and unacceptable”, while the European Union’s foreign policy chief, Kaja Kallas, said the treatment of the detainees was “degrading and wrong”.

Even Mike Huckabee, the US ambassador to Israel and a stalwart supporter of Prime Minister Benjamin Netanyahu, called Ben-Gvir’s actions “despicable”, saying he had “betrayed the dignity of his nation”.

Netanyahu himself also publicly rebuked Ben-Gvir. He said Israel had the right to stop the flotilla, but the minister’s behaviour had damaged Israel’s image and did not reflect the country’s values.

Even though international lawyers like myself have expressed concern about this on multiple occasions, it bears repeating: international law matters in conflict zones.

So, what obligations does Israel have to treat those detained by its forces, and did the country violate the law?

Why were the activists detained?

Israeli forces began intercepting the Gaza-bound Global Sumud flotilla on Monday in international waters off the coast of Cyprus. Dozens of boats were stopped as they attempted to challenge Israel’s maritime blockade of Gaza.

The flotilla reportedly carried more than 400 activists from over 40 countries. Those on board included humanitarian volunteers, medical personnel, peace activists and civil society figures. Organisers said the vessels were carrying humanitarian relief supplies, including food, medicine and other aid intended for Palestinian civilians affected by the war and blockade of Gaza.

Israel disputed the flotilla’s aid-delivery purpose and described it as “a PR stunt at the service of Hamas”.

After those on board were arrested, they were reportedly subjected to violence, with some suffering suspected broken ribs and other injuries.

In a post on X, the Israeli Foreign Ministry claimed Israel was acting in full accordance with international law.

What does the law say?

Under international humanitarian law, those involved in the transport and distribution of relief supplies must be respected and protected during armed conflict. They are to be treated as civilians so long as they do not directly take part in hostilities.

Bringing aid to the civilians of Gaza does not amount to “direct participation in hostilities”. In fact, the International Court of Justice has ordered Israel to allow aid into Gaza given their obligations under the Genocide Convention.

International humanitarian law also says civilians may not be detained arbitrarily in conflict zones. If civilians are detained, however, they have certain rights under international law. They must:

• be informed of the reasons for their detention

• be able to challenge the detention decision

• receive adequate food, hygiene and medical care

• be given access to lawyers and consular representatives, in addition to contact with their families, and

• be held in conditions consistent with health and humanity.

Internment of civilians is only permitted when “absolutely necessary” for security reasons. It must end once those reasons no longer exist.

In addition, civilians detained during armed conflict must be treated humanely at all times.

They are to be protected from:

• violence and torture

• cruel, humiliating or degrading treatment

• intimidation and insults, and

• “public curiosity” or degrading public exposure.

The phrase “public curiosity” has historically been understood to prohibit humiliating displays of detainees for propaganda, intimidation or public spectacle.

Intentional attacks against humanitarian personnel can amount to war crimes under the Rome Statute of the International Criminal Court.

Why does this matter?

The public humiliation and degrading treatment of the activists shown in the footage must be scrutinised and investigated. And Israeli officials must comply with their obligations under the law.

These protections exist precisely to preserve a minimum standard of humanity during conflict, and to ensure civilians and humanitarian actors are not stripped of their dignity for political theatre, intimidation or punishment.

When such conduct is normalised or left unchallenged, it risks undermining the broader international legal framework designed to protect all civilians caught up in armed conflict.

Shannon Bosch, Associate Professor (Law), Edith Cowan University

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

Reviewed by Irfan Ahmad.

Read next: 

• Technology usually creates jobs for young, skilled workers. Will AI do the same?

• Fears of helping the enemy are blocking international agreements on AI in weapons systems

• Google’s AI Search Has Struggled With One Religious Question for Years

by External Contributor via Digital Information World

Technology usually creates jobs for young, skilled workers. Will AI do the same?

By Peter Dizikes, MIT News

A new study of the postwar U.S. shows which kinds of workers historically filled new tech-enabled jobs.

Image: Vitaly Gariev - unsplash

At any given time, technology does two things to employment: It replaces traditional jobs, and it creates new lines of work. Machines replace farmers, but enable, say, aeronautical engineers to exist. So, if tech creates new jobs, who gets them? How well do they pay? How long do new jobs remain new, before they become just another common task any worker can do?

A new study of U.S. employment led by MIT labor economist David Autor sheds light on all these matters. In the postwar U.S., as Autor and his colleagues show in granular detail, new forms of work have tended to benefit college graduates under 30 more than anyone else.

“We had never before seen exactly who is doing new work,” Autor says. “It’s done more by young and educated people, in urban settings.”

The study also contains a powerful large-scale insight: A lot of innovation-based new work is driven by demand. Government-backed expansion of research and manufacturing in the 1940s, in response to World War II, accounted for a huge amount of new work, and new forms of expertise.

“This says that wherever we make new investments, we end up getting new specializations,” Autor says. “If you create a large-scale activity, there’s always going to be an opportunity for new specialized knowledge that’s relevant for it. We thought that was exciting to see.”

The paper, “What Makes New Work Different from More Work?” is forthcoming in the Annual Review of Economics. The authors are Autor; Caroline Chin, a doctoral student in MIT’s Department of Economics; Anna M. Salomons, a professor at Tilburg University’s Department of Economics and Utrecht University’s School of Economics; and Bryan Seegmiller PhD ’22, an assistant professor at Northwestern University’s Kellogg School of Management.

And yes, learning about new work, and the kinds of workers who obtain it, might be relevant to the spread of artificial intelligence — although, in Autor’s estimation, it is too soon to tell just how AI will affect the workplace.

“People are really worried that AI-based automation is going to erode specific tasks more rapidly,” Autor observes. “Eroding tasks is not the same thing as eroding jobs, since many jobs involve a lot of tasks. But we’re all saying: Where is the new work going to come from? It’s so important, and we know little about it. We don’t know what it will be, what it will look like, and who will be able to do it.”

“If everyone is an expert, then no one is an expert”

The four co-authors also collaborated on a previous major study of new work, published in 2024, which found that about six out of 10 jobs in the U.S. from 1940 to 2018 were in new specialties that had only developed broadly since 1940. The new study extends that line of research by looking more precisely at who fills the new lines of work.

To do that, the researchers used U.S. Census Bureau data from 1940 through 1950, as well as the Census Bureau’s American Community Survey (ACS) data from 2011 to 2023. In the first case, because Census Bureau records become wholly public after about 70 years, the scholars could examine individual-level data about occupations, salaries, and more, and could track the same workers as they changed jobs between the 1940 and 1950 Census enumerations.

Through a collaborative research arrangement with the U.S. Census Bureau, the authors also gained secure access to person-level ACS records. These data allowed them to analyze the earnings, education, and other demographic characteristics of workers in new occupational specialties — and to compare them with workers in long standing ones.

New work, Autor observes, is always tied to new forms of expertise. At first, this expertise is scarce; over time, it may become more common. In any case, expertise is often linked to new forms of technology.

“It requires mastering some capability,” Autor says. “What makes labor valuable is not simply the ability to do stuff, but specialized knowledge. And that often differentiates high-paid work from low-paid work.” Moreover, he adds, “It has to be scarce. If everyone is an expert, then no one is an expert.”

By examining the census data, the scholars found that back in 1950, about 7 percent of employees had jobs in types of work that had emerged since 1930. More recently, about 18 percent of workers in the 2011-2023 period were in lines of work introduced since 1970. (That happens to be roughly the same portion of new jobs per decade, although Autor does not think this is a hard-and-fast trend.)

In these time periods, new work has emerged more often in urban areas, with people under 30 benefitting more than any other age category. Getting a job in a line of new work seems to have a lasting effect: People employed in new work in 1940 were 2.5 times as likely to be in new work in 1950, compared to the general population. College graduates were 2.9 percentage points more likely than high school graduates to be engaged in new work.

New work also has a wage premium, that is, better salaries on aggregate than in already-existing forms of work. Yet as the study shows, that wage premium also fades over time, as the particular expertise in many forms of new work becomes much more widely grasped.

“The scarcity value erodes,” Autor says. “It becomes common knowledge. It itself gets automated. New work gets old.”

After all, Autor points out, driving a car was once a scarce form of expertise. For that matter, so was being able to use word-processing programs such as WordPerfect or Microsoft Word, well into the 1990s. After a while, though, being able to handle word-processing tools became the most elementary part of using a computer.

Back to AI for a minute

Studying who gets new jobs led the scholars to striking conclusions about how new work is created. Examining county-level data from the World War II era, when the federal government was backing new manufacturing in public-private partnerships throughout the U.S., the study shows that counties with new factories had more new work, and that 85 to 90 percent of new work from 1940 to 1950 was technology-driven.

In this sense there was a great deal of demand-driven innovation at the time. Today, public discourse about innovation often focuses on the supply side, namely, the innovators and entrepreneurs trying to create new products. But the study shows that the demand side can significantly influence innovative activity.

“Technology is not like, ‘Eureka!’ where it just happens,” Autor says. “Innovation is a purposive activity. And innovation is cumulative. If you get far enough, it will have its own momentum. But if you don’t, it’ll never get there.”

Which brings us back to AI, the topic so many people are focused on in 2026. Will AI create good new jobs, or will it take work away? Well, it likely depends how we implement it, Autor thinks. Consider the massive health care sector, where there could be a lot of types of tech-driven new work, if people are interested in creating jobs.

“There are different ways we could use AI in health care,” Autor says. “One is just to automate people’s jobs away. The other is to allow people with different levels of expertise to do different tasks. I would say the latter is more socially beneficial. But it’s not clear that is where the market will go.”

On the other hand, maybe with government-driven demand in various forms, AI could get applied in ways that end up boosting health care-sector productivity, creating new jobs as a result.

“More than half the dollars in health care in the U.S. are public dollars,” Autor observes. “We have a lot of leverage there, we can push things in that direction. There are different ways to use this.”

This research was supported, in part, by the Hewlett Foundation, the Google Technology and Society Visiting Fellows Program, the NOMIS Foundation, the Schmidt Sciences AI 2050 Fellowship, the Smith Richardson Foundation, the James M. and Cathleen D. Stone Foundation, and Instituut Gak.

Reprinted with permission of MIT News.

Reviewed by Irfan Ahmad.

Read next: Fears of helping the enemy are blocking international agreements on AI in weapons systems

by External Contributor via Digital Information World

Fears of helping the enemy are blocking international agreements on AI in weapons systems

Mark Tsagas, University of East London

Walking the dog: a US service member patrols with a Ghost Robotics Vision 60 prototype at Nellis Air Force Base in Nevada. Tech. Sgt. Cory Payne / US Air Force - Public Domain

The third in a series of military AI summits was held in La Coruña, Spain in February 2026. The aim of the meeting was to convert previously agreed principles on the military use of AI into action. The summit was attended by government officials, military personnel, representatives from industry and researchers from thinktanks.

The goal of many experts and policymakers in this area is to usher countries towards a regulatory framework on using machine intelligence in warfare. To this end, the latest Responsible AI in the Military Domain (REAIM) summit presented a non-binding commitment for countries to sign.

The REAIM agreement affirmed the need for human oversight of military AI systems, called for countries to carry out risk assessments and robust testing, and committed to transparency on how decisions are made when using AI in conflicts.

The reasoning behind such recommendations is sound. However, translating such a framework from plan to action faces multiple hurdles. Ultimately, less than half of the countries represented at this year’s REAIM summit signed the non-binding commitment.

To understand why, it’s instructive to look at what happened at the 80th UN General Assembly held in New York in December 2025. At the meeting, members of the assembly’s first committee voted overwhelmingly to approve two resolutions calling for greater international scrutiny of the risks from military uses of AI. However, the US and Russia notably opposed the resolutions.

The US had been a signatory to earlier REAIM summit commitments. But this year, the US and China both declined to sign it. There seems little doubt that this helped fuel the hesitancy of other countries.

The Netherlands’ defence minister Ruben Brekelmans put it succinctly when he said that governments face a “prisoner’s dilemma”. This is a concept in game theory where two rational individuals face competing incentives to cooperate with or betray one other.

Countries are effectively having to implement responsible restrictions on military AI without subjecting their armed forces to limitations that could be exploited by a less conscientious enemy.

An important sticking point is the deployment of autonomous AI systems in warfare. The idea of autonomous weapons systems, which make decisions without input from a human, remains a grave concern for many interested parties on this issue.

There continues to be a consensus against using such weapons. But countries can’t reach a common position over how to define them, particularly so-called lethal autonomous weapons systems – or Laws for short. These are often characterised as “killer robots”, though a more detailed description remains elusive.

A uniform definition for such systems could be an important first step towards a discussion on regulation. But, despite efforts by academic experts to draft and amend flexible definitions, countries remain too far apart on the characteristics they ascribe to these weapons.

The impasse is informed by a fear that accepting a definition could restrict countries’ militaries on the battlefield – threatening national security.

Testing grounds for tech

Existing legal mechanisms, such as international humanitarian laws, already prohibit the irresponsible and unethical use of military AI – in theory, at least. But how these laws would function in practice when applied to real world scenarios is uncertain.

The ongoing Russia-Ukraine war, the war in Gaza and the more recent escalation in Iran are being used by militaries as testing grounds for such technology.

The Lavender intelligence gathering and targeting software, used by Israel in Gaza, and Anthropic’s AI Model Claude, used by the US in Iran, demonstrate the rapid pace of advancement in AI-powered data gathering and analysis. This can help military planners make quicker decisions.

Drone warfare – AI assisted, autonomous and semi-autonomous – has grown at an equally rapid rate. This emerging technology is evolving significantly faster than the potential rules that could govern its use.

There’s a recurring argument that humans in the loop can operate as effective safeguards against the misuse of military AI systems. But as human overseers become familiar with the AI systems they use, their engagement may slip, causing them to become detached from the process.

As this happens, they may start to view real people as mere objects on a screen. This effect is known as automation bias. In such instances, human oversight could cease to be meaningful and instead lead to the simple rubber stamping of recommendations made by AI.

Additionally, the downsides of AI technology, such as bias, misinformation and disinformation generated by the systems themselves, and the erosion of human judgement resulting from overreliance on these systems, are not easy to solve after they enter use. This is why the REAIM summit commitment recommended risk assessments and robust testing before AI systems are adopted by militaries.

Without regulation, the risk of harm caused by AI systems remains significant. The severity of such risks balloons in magnitude when they are applied to military contexts. Miscalculations can lead to unintended escalation, as well as civilian deaths.

Mark Tsagas, Senior Lecturer in Law, Cybercrime & AI Ethics, University of East London

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

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

Dark patterns on the web are designed to manipulate you – why aren’t they all illegal?

Gregory M. Dickinson, University of Nebraska-Lincoln; Institute for Humane Studies

Image: Nubelson Fernandes - unsplash

You open a free app to do one simple thing. Before you even start, a full-screen message asks whether you want to try the paid version. The “Start free trial” button is large, bright and hard to miss. The option to keep using the free version is smaller, buried at the bottom. The same prompt appears again tomorrow. And the day after that.

A lot of people look at screens like that and think, “Surely this has to be illegal.” We even have a name for them, “dark patterns.” They feel pushy. They waste time. They seem designed to wear you down. But in most cases, they are perfectly lawful.

“Dark pattern” is not a legal term with a clear boundary. It is a broad label for digital designs that nudge, pressure, confuse or trap users. As a legal scholar who studies consumer protection and digital design, I think the most important thing for readers to understand is that the label “dark pattern” covers a broad spectrum.

Some of that spectrum is just annoying. Some of it is aggressive salesmanship. And some of it crosses the line into deception or coercion. Federal and state consumer protection laws are mostly aimed at that last category. They do not ban every design choice people dislike, only those that trick or coerce.

Annoying isn’t illegal

That reality may sound unsatisfying, but it is not unusual. Offline life is full of things that are irritating but not unlawful. Think of the cashier who asks whether you want to sign up for the store credit card, then points out the discount you are turning down, then asks again. Most people know exactly what is happening. They roll their eyes, say no and try to shop somewhere else next time.

The same is true online. A repeated pop-up can be obnoxious. A guilt-inducing button can be tacky. But consumers recognize ordinary annoyance for what it is. In many cases, the market answer is simple: Close the app, ignore the pitch or take your business elsewhere.

Similarly, law does not ban persuasive sales pitches just because they are effective. A car salesperson who keeps steering you toward the upgraded model is trying to influence your choice. So is the airline clerk who offers travel insurance. So is the restaurant server who asks whether you want dessert. Salesmanship is nothing new. Digital design often borrows from familiar techniques.

That helps explain why lawmakers cannot simply outlaw “manipulation.” And so many interfaces are built to persuade, openly and lawfully.

What crosses the line

What the federal FTC Act and analogous state consumer-deception statutes usually care about is not whether a design is annoying. They focus on whether the design is likely to mislead a reasonable consumer. That is the core idea in modern consumer protection law.

So a design is likelier to be unlawful when it hides key facts, makes an optional choice look mandatory or tricks people about the effect of the button they are pressing. A fake countdown timer, a disguised ad, a misleading one-click purchase button or a cancellation path that looks finished when it is not are all different from ordinary hard selling. Those designs do not just pressure users; they can deceive them.

That is also why the app maker’s intent is not always the key question. In many consumer protection cases, a company does not get a free pass just because no one said, “Let’s trick people.” The legal question is often about effect: What would a reasonable user likely understand from this screen?

Research on dark patterns reinforces that concern. Even relatively mild designs can push people into choices they would not otherwise make. And regulators have increasingly focused on subscription flows, hidden fees and cancellation obstacles for exactly that reason.

Why it feels like dark patterns are everywhere

One reason people might think there are no laws against dark patterns is that they see them so often. But that frequency reflects that the term covers a wide range of conduct, from lawful nagging to outright deception.

It also reflects enforcement limits. Regulators cannot chase every irritating screen on every app and website. They have to prioritize the worst cases. That leaves a lot of borderline conduct in the wild, which makes the whole problem feel bigger and murkier to ordinary users.

So when people ask why there is not a law against dark patterns, the best answer is that there already is, but the law does not prohibit every annoying or high-pressure design. It targets lies, misleading cues and coercive obstacles.

That line can be fuzzy. But the fuzziness is not a mistake. It is what you get when the law tries to separate persuasion from deception in a world full of both.

Gregory M. Dickinson, Assistant Professor of Law, University of Nebraska-Lincoln; Institute for Humane Studies

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

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

Surfshark Report Raises Concerns Over Difficulty of Opting Out of AI Training on Social Media

Reviewed by Irfan Ahmad.

A May 12 study by cybersecurity company Surfshark found that many social media platforms either enable AI training on user data by default or require users to complete lengthy opt-out procedures. The research examined 10 widely used platforms, reviewing app privacy policies and the number of actions needed to block AI training where such controls existed.

According to the report, TikTok required 19 actions to request an opt-out, while Facebook and Instagram each required eight. Snapchat, LinkedIn, X and Pinterest offered shorter processes but kept AI training enabled by default. The study also said Reddit provided no user opt-out option for AI model training.

Surfshark said the effectiveness of opt-out requests may depend on local privacy laws, with stronger protections available in the European Union (EU), the European Economic Area (EEA), and the United Kingdom (UK) under GDPR. In the company’s press statement sent to DIW, Research and Insights Team Lead Luís Costa said:

"If you've ever shared content on social media, it's highly probable your data is being used to train AI models." The statement added, "Our findings reveal that while social media connects us globally, these platforms also exploit user-generated content as a resource for AI training, often without clear, user-friendly opt-out options."

Read next:

• Google’s AI Search Has Struggled With One Religious Question for Years

• More and more websites want proof you’re human. Blame the bots
by AI Analysis via Digital Information World

More and more websites want proof you’re human. Blame the bots

Yang Xiang, Swinburne University of Technology

Image: DIW. 

You’re trying to book concert tickets before they sell out. You click the link and before you can make the payment, you’re asked to identify traffic lights, bicycles or blurry crosswalks in a grid of tiny images.

Again.

For many people, this has become a routine part of life. Logging into financial apps, shopping online or creating accounts increasingly involves “proving you are human”.

These systems are known as CAPTCHA. Why are they everywhere?

The short answer is that websites are fighting a rapidly escalating war against bots: automated software that imitate human behaviour online. And thanks to advances in artificial intelligence (AI), those bots are becoming even smarter, cheaper and harder to detect than ever before.

Why websites need proof you are human

Huge amounts of online traffic now come from automated systems. Some are helpful, such as search engine crawlers indexing pages for Google search.

Others are far less welcome, and may involve phishing, spam, fake accounts, passwords violation, misinformation, and distributed denial of service attacks overloading web servers. In some areas, AI agents now generate automated online traffic that exceeds human traffic altogether. Modern AI systems can generate convincing text, imitate browsing patterns and even solve some CAPTCHA puzzles.

At the same time, companies are increasingly worried about bots scraping online content to train AI systems.

As a result, more websites are adding verification systems simply to keep abuse under control.

How CAPTCHA actually works

CAPTCHA stands for “Completely Automated Public Turing test to tell Computers and Humans Apart”. The original idea was simple: give users a task humans find easy, but computers find difficult.

Early CAPTCHA systems often involved distorted text. Later versions switched to image-recognition tasks such as selecting all the squares containing traffic lights or bicycles. Google’s reCAPTCHA became one of the best-known examples. Earlier versions even helped digitise books and improve street-view image recognition while users solved puzzles.

But computer vision has improved rapidly in recent years. Advances in AI mean bots can now solve many traditional CAPTCHA challenges surprisingly well. Researchers have repeatedly shown that modern AI systems can bypass some CAPTCHA systems with high success rates.

That is why today’s CAPTCHA systems rely less on puzzles and more on behavioural analysis.

When users click the CAPTCHA link, the system analyses many background signals, such as mouse movements, typing speed, IP addresses, device information, and interaction timing that reflect human behaviours. Humans tend to behave in inconsistent ways. Bots are usually more predictable.

If the system is sufficiently confident you are human, you may never see an image puzzle at all. But if something appears suspicious, the system may trigger harder tests.

Moving beyond traditional CAPTCHA puzzles

While some bots now use AI capable of solving image-recognition tasks, others simply outsource CAPTCHA solving to cheap human labour services, where real people complete challenges for a small payment. This has turned CAPTCHA into an ongoing arms race. That may explain why CAPTCHA tests often feel harder and more frustrating than they used to.

As AI continues to improve, websites will likely move beyond traditional CAPTCHA puzzles. Future systems may increasingly rely on behavioural biometrics, such as typing rhythm or scrolling style, device verification systems, invisible background risk scoring, and AI systems designed to detect other AI systems.

In many cases, users may no longer even notice the verification process happening.

CAPTCHA tests may seem like a minor annoyance, but they reflect a much larger paradigm shift online. For decades, websites largely assumed visitors were human. Increasingly, that assumption no longer holds. As AI-generated traffic continues to grow, proving we are human online may become an even more common part of everyday life.

Yang Xiang, Professor, Computer Science, Swinburne University of Technology

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

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

Google’s AI Search Has Struggled With One Religious Question for Years

In the summer of 2025, I was trying to locate public-domain image platforms that handled Creative Commons licensing properly.

That search eventually led me to Wikimedia Commons, and into another problem entirely.

In Pakistan, parts of the site were intermittently inaccessible. Sometimes pages loaded. Sometimes thumbnails appeared while full media pages failed. At first, it looked like an ordinary connectivity issue. It wasn’t.

Official explanations were vague. Online discussions pointed in different directions. Some referenced blasphemy disputes. Others blamed Wikipedia moderation or controversial religious material. None fully explained why one of the world’s largest free media repositories had become entangled in a national access dispute.

Months later, while revisiting those debates, I stumbled into a much older controversy, one involving Google Search, English Wikipedia, and a disputed religious question that had been circulating online since at least 2020.

Search Google for “current caliph of worldwide muslim community” or "present caliph of Islam" and the answer does not stay stable.

Change the wording slightly, and Google’s AI Overview system responds differently.

Different wording. Same question. Different answer.

In some cases, Google’s AI Overviews state that no universally recognized caliph exists in mainstream Islam today.

Slight wording shifts often changed the answer completely

Searches such as:

• “who is caliph of Islam”
• “current caliph of Islam”
• “does Islam have a caliph today”
• “current Islamic caliphate”

often generate relatively cautious AI Overviews.

In repeated tests conducted across multiple sessions, Google frequently explained that the historical Ottoman caliphate formally ended in 1924, that no single universal caliph is recognized by the vast majority of the world’s roughly 1.9 billion Muslims today, and that some groups, including the Ahmadiyya Community, maintain their own internal caliphate structures.

The supporting search results were similarly broad and historical, frequently linking to sources such as Encyclopaedia Britannica’s entry on the caliphate and Wikipedia’s historical lists of caliphs.

But small wording changes often shifted the framing completely.

Searches including, "current caliph of global community" and “present caliph of worldwide muslims”, in a number of cases returned AI-generated summaries centered on Mirza Masroor Ahmad, the fifth caliph and leader of the Ahmadiyya Community.

The summaries were polished and authoritative in tone. They described his election in 2003, humanitarian campaigns, and global leadership role.

Sometimes the summaries later clarified that mainstream Sunni and Shia do not recognize him as a universal caliph.

Sometimes the clarification appeared only near the end of the response, behind a “Show more” prompt.

In some versions, it did not appear at all.

The discrepancy appeared repeatedly across searches.

After dozens of searches, the pattern became difficult to dismiss

Historically, the term “caliph” referred to leadership claiming succession to the Islamic Prophet Muhammad in guiding the Muslim community (Ummah). For centuries, different dynasties claimed the title. Today, however, no single universally recognized caliph exists across the global Muslim population.

Yet Google’s systems often treated the question as though it pointed toward one identifiable figure.

Some less conventional searches produced even more unusual results.

Queries such as:

• “current caliph and top leader in the world of technology”
• “present caliph of Islam in Google head office”

still frequently pointed toward Mirza Masroor Ahmad before expanding into unrelated discussions involving satellite television networks, or international speeches.

Another Google and Gemini search, “present caliph messiah in the global community”, produced an answer emphasizing peace advocacy, parliamentary visits, disaster relief work, and international leadership activities before later clarifying that the framing reflected Ahmadiyya beliefs.

One answer read less like a response to a disputed religious question than a polished leadership profile.

Across multiple searches, a recurring pattern was observed: ambiguous religious queries repeatedly drifted toward the same structured entity.

In practice, the results suggested that Google’s systems may prioritize the most strongly structured or prominently linked “caliph” profile available within its search ecosystem.

Google’s search systems favor recognizable entities

Google has publicly described Search as increasingly dependent on entity understanding and structured information systems.

In its Knowledge Graph announcement, the company explained that Google aims to understand “things, not strings”, connecting words to identifiable people, places, organizations, and concepts.

That approach works well for many factual searches.

Religious authority questions are difficult because different groups believe different things.

Most of the information appearing in these AI summaries was factually accurate within the Ahmadiyya context.

The problem was how confidently the system framed a disputed religious question.

Taha Yasseri, a Professor and Chair of Technology and Society at Trinity College Dublin and Technological University Dublin, said the behavior reflects a broader structural challenge in generative search systems.

“In domains such as religion, history, or identity, disagreement is often not a flaw in the information ecosystem but an inherent feature of the subject itself,” Yasseri wrote in comments provided for this article.

He argued that AI systems optimized for “coherent and concise answers” can create an “illusion of consensus” by compressing disagreement into a single authoritative-seeming response.

According to Yasseri, systems handling contested topics should make disagreement visible, attribute viewpoints clearly, and communicate uncertainty where no universal consensus exists.

Google’s systems are generally designed to deliver direct answers.

Some questions, however, were never universally answered in the first place.

The controversy was already public before AI Overviews existed

Complaints about the issue were publicly visible years before Google launched AI Overviews.

In December 2020, users began posting complaints on Google Search Help forums after searches for “present caliph of Islam” surfaced the Ahmadiyya leader prominently.

The discussion accumulated substantial engagement, with more than 150 users marking “I have the same question.”

Some comments devolved into sectarian hostility directed at the Ahmadiyya community.

Others focused directly on Google’s search behavior and questioned why disputed religious information was being presented so definitively.

The dispute had already become publicly contentious by late 2020.

Ahmadiyya-affiliated publications published articles defending the search result as evidence of the movement’s global religious legitimacy, while critics organized online campaigns urging users to report the Google answer as misleading.

What began as a search-quality issue increasingly became a symbolic dispute over religious authority, visibility, and representation online.

Then Wikipedia publicly stepped in

One volunteer Google product expert responding in the forum thread eventually pointed users toward a clarification page created by Wikipedia itself.

That page became one of the clearest public acknowledgments of the issue.

In a dedicated notice titled Ahmadiyya Caliphate information, Wikipedia states that Google searches for “current caliph of Islam” or similar phrases may “incorrectly display” the Wikipedia article about Mirza Masroor Ahmad.

The clarification then makes the distinction explicit:

“This issue is caused by Google’s algorithms incorrectly interpreting Wikipedia’s article on the Ahmadiyya Caliphate. This misinformation does not come from Wikipedia...”

The notice states that the issue had existed since at least December 2020 and remained active in May 2026.

The behavior survived years of public complaints, multiple Google Search updates, and the arrival of AI-generated search summaries.

Wikipedia, neutrality, and AI systems inheriting structure

The controversy also intersected with longstanding debates about neutrality and representation on Wikipedia itself.

In a broader discussion about Wikipedia’s editorial model, a recent analysis published by The Conversation argued that the platform’s neutrality system depends on ongoing negotiation over sourcing, balanced representation, and editorial weight.

Rob Nicholls, a researcher at the University of Sydney, said in an email to this reporter that the behavior may reflect broader limitations in how AI systems process and reuse information from widely used online sources.

Nicholls noted that Wikipedia is widely used in AI training and information retrieval systems because of its permissive licensing structure and enormous volume of structured content.

“AI chat services miss subtleties,” Nicholls wrote in comments provided for this article. “This may also seem like reinforcing stereotypes or falsehoods.”

He also warned that consensus-driven systems can flatten nuance or reinforce “groupthink” when information about a topic is unevenly represented.

How the controversy reached Pakistan

Because many users blamed Wikipedia for Google’s search outputs, the issue eventually became entangled with wider online disputes involving religious authority and content moderation.

In February 2023, Pakistan temporarily blocked Wikipedia amid disputes involving allegedly sacrilegious material.

The episode was covered by outlets including The Express Tribune, and Gizmodo. The restriction was later lifted after intervention from Pakistan’s prime minister.

Discussions inside Wikimedia communities later referenced broader concerns about Wikimedia Commons accessibility inside Pakistan and debates surrounding religious-content moderation online.

Google acknowledged AI mistakes, but the issue persisted

Google’s AI Overviews are generated using large language models and related search systems designed to synthesize information into concise answers.

Google openly acknowledges the technology’s limitations.

In its own AI Overviews documentation, the company states:

“AI Overviews can and will make mistakes.”

The same documentation advises users to verify important information using multiple sources and compare answers by rephrasing questions.

That advice becomes unusually relevant here because slight wording changes can produce entirely different interpretations of the same religious issue.

Some searches now generate historically grounded summaries acknowledging that mainstream Islam has no universally recognized caliph today.

Others still drift toward confident entity resolution centered around one movement’s leadership structure.

The inconsistency suggests multiple overlapping systems interacting imperfectly:

• query interpretation,
• knowledge graph matching,
• entity resolution,
• generative summarization,
• ranking systems prioritizing highly structured and heavily linked information,
• and AI safeguards attempting to balance certainty with nuance.

Other search engines handled the ambiguity inconsistently as well.

At times, DuckDuckGo, Yandex, and Microsoft Bing began by clarifying that no universally recognized caliph exists in mainstream Islam before separately introducing the Ahmadiyya position. At other times, they produced results resembling Google’s framing.

Search systems appeared to repeatedly converge on a single reference point for a concept that lacks one universally accepted definition.

Requests for comment

Between multiple reporting sessions conducted during 2026, this reporter contacted Google Press requesting clarification about how AI Overviews handle disputed religious questions, whether sensitive religious queries receive contextual review, and why years of public feedback appeared not to have resolved the issue. Google did not respond to multiple requests for comment by publication time. During later testing conducted after those outreach attempts, some AI Overviews appeared to shift toward more historically framed responses emphasizing that no universally recognized caliph exists today. Because Google’s AI-generated search outputs can change over time, it remains unclear whether those shifts reflected routine system updates, query variation, experimentation, or broader adjustments to AI Overview behavior.

This reporter also contacted media representatives affiliated with the Ahmadiyya Community requesting clarification regarding how the community distinguishes its internal caliphate structure from wider Muslim representation, and whether it had previously communicated with Google regarding related search terminology. No response was received by publication time.

The Wikimedia Foundation was also contacted regarding how Wikipedia handles contested religious authority structures and ensures that internal doctrinal perspectives and external viewpoints are represented with appropriate due weight, particularly when external AI systems extract structured content from encyclopedia articles. The Wikimedia Foundation did not respond to multiple requests for comment by publication time.

Conclusion

For most users, the issue may appear obscure, one unusual religious query among billions processed every day.

But the episode reveals something larger about modern AI search systems.

Search engines increasingly do more than organize information.

They interpret it. They summarize it. They compress disagreement into readable answers.

And in some cases, they present answers with a level of certainty that may not fully reflect underlying disagreement or ambiguity.

For years, asking Google who leads the world’s Muslims has produced answers that sound definite, even though the question itself has no single agreed-upon answer.

Methodology

Searches referenced in this article were conducted across multiple sessions between 2025 and 2026 using Google Search and AI Overviews, including signed-out browser tests and incognito sessions where available.

Because AI-generated search results can vary over time, by location, language settings, and between users and screenshots were retained during reporting.

Limits of observation

The searches described here reflect a limited set of queries conducted across specific sessions and environments. Google Search and AI Overviews can vary based on factors such as location, language settings, personalization signals, indexing updates, and ongoing model changes. As a result, the outputs observed should be understood as illustrative examples of system behavior rather than a comprehensive or fixed representation. These systems are actively evolving, and similar queries may produce different responses over time as ranking and generative models are updated.

Timeline

• December 2020: Complaints about Google’s “present caliph of Islam” search results appear publicly on Google Search Help forums.
• 2020–2022: Wikipedia publishes clarification notices stating that disputed search outputs do not originate from Wikipedia itself.
• February 2023: Pakistan temporarily blocks Wikipedia amid broader religious-content disputes.
• 2024: Google expands AI-generated summaries through AI Overviews.
• 2025–2026: Variations of the disputed search behavior remain publicly visible.

AI Disclosure: This article was prepared with assistance from AI tools for drafting and language support. The author is solely responsible for the accuracy, sourcing, and final editorial content.

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by Irfan Ahmad via Digital Information World