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Tuesday, May 26, 2026
Why people share live locations and how it changes modern communication
Image: Ron Lach - Pexels
Mobile apps that allow people to share their location with others have become increasingly popular. But how and why do we use these apps, and what are the implications for interpersonal communication? That’s the topic of a new study from the University of Illinois Urbana-Champaign.
“I was teaching a class on relationship development to undergraduate students, and I made a comment about location sharing. They all got very animated, sharing experiences and asking questions about use cases. I realized there was very little research about this topic, so I decided to conduct a study,” said lead author Brian Ogolsky, professor in the Department of Human Development and Family Studies at U. of I.
“I believe knowing how people use these technologies helps us to understand the scripts and the processes that underpin relationships, how they are changing, and what that means for how we relate to each other.”
Ogolsky and his colleagues conducted an online survey with individuals across the U.S. and the U.K., asking them to describe their location sharing practices.
Respondents on average shared their location with 3.86 people, with a span ranging from 1 to 83. The majority reported using the Find My app for iPhone, followed by Google Maps, Life360, Snapchat, and WhatsApp.
People most often shared their location with their romantic partner, then friends, siblings, parents, children, other family members, and roommates.
The researchers organized the reasons people were sharing their location into four main categories: safety, practicality, casual use, and relationship processes.
Safety was the main reason for sharing location with immediate family, parents, and children.
“Respondents said it helps them feel safer knowing where someone is. That’s unsurprising; however, it’s really an illusion of safety. Knowing where my partner is 50 miles from here does not mean I can help them in a pinch, or that I can get somebody to help them. It may be more about peace of mind than actual safety,” Ogolsky said.
For romantic partners and friends, practicality was the most common reason for sharing. That included convenience and planning, such as what time to make dinner, or coordinating who picks up the kids. Respondents also mentioned keeping track of people who are traveling, or interacting with others who live or work in different places.
Casual use included sharing location for fun and novelty. Some respondents said it seemed interesting or entertaining, and there was no specific intent. For example, they would share location with everyone in their friend group and then forget about it.
Finally, the researchers identified relationship processes as a separate category, indicating usage specifically intended to maintain, support, and manage a relationship. This could be about trust, honesty, and open communication. A few people also mentioned pressure or expectations from their partner or family members about their roles and responsibilities.
Ogolsky pointed out there are potential drawbacks when technology replaces human interaction.
“Our findings highlight we’re heading towards a world where technological changes will dictate how and when we communicate. Location sharing is moving from primarily safety-related causes into the relationship realm, where it alters communication,” he said.
“You can check where someone is and decide you don’t want to bother them, so you don’t call or text. It takes away the ability of the person to say whether they would like to talk right now, and removes interpersonal negotiation.”
People come to depend on technology; for example, if you plan to meet someone at a concert and your phone dies, you may not be able to find each other. Location sharing with friend groups can also create a sense of FOMO, the fear of missing out, if you notice other people getting together without you.
Giving others access to your location also raises privacy concerns. However, this appears to be less important to younger generations who have grown up in a world where they have been surveilled by tech companies since they were born. Their idea of what should be private information is fundamentally different than older generations, Ogolsky noted.
There are also implications regarding whether location sharing information can be used as evidence in criminal court cases, and it can be misused in abusive relationships.
Ultimately, most people adopt a new technology because they think it's going to make their lives easier.
“There is something to be said for streamlining the minutia of relationships. A lot of people do not like planning; they do not like waiting. With location sharing, they can get information about others without intruding, they can be where they need to be at the right times. If they can squeeze in one more thing, that can be a real benefit for some people,” Ogolsky concluded.
The study, “Near, Far, Wherever You Are: With Whom and Why Do People Use Location Sharing in Relationships,” is published in the Journal of Social and Personal Relationships [DOI: 10.1177/02654075261446344]. This research was supported by Hatch funding from USDA’s National Institute of Food and Agriculture.
This article was originally published by the University of Illinois Urbana-Champaign, ACES NEWS and republished here with permission.
Reviewed by Irfan Ahmad.
Read next: Legal pressure key to removing nonconsensual nudity online
by External Contributor via Digital Information World
Pope Leo warns of AI’s risks to humanity in his first encyclical
Pope Leo XIV has just declared artificial intelligence one of the defining moral challenges of our time, in his first encyclical: a formal letter intended to guide moral, social and theological thought. Titled Magnifica Humanitas (Magnificent Humanity), it argues technology must serve humanity, rather than concentrate power or weaken human dignity.
He presented it at the Vatican alongside AI developer Christopher Olah, cofounder of Anthropic, who acknowledged that companies like his need moral guidance to guard against “incentives and constraints that can sometimes conflict with doing the right thing”, the New York Times reported.
“Technology is not simply a tool,” read the roughly 42,300-word open letter. “When it becomes the standard by which everything is judged, it begins to dictate what matters and what can be discarded, reducing creation to an object of exploitation and human beings to mere cogs in a system driven toward ever greater efficiency.”
It warns that AI is never truly neutral, but “takes on the characteristics of those who devise, finance, regulate and use it”. And it calls for ethical oversight, social justice, protection of workers, responsible governance and peace.
Automated warfare
The encyclical criticises the use of AI in warfare, calling for imposing the “most rigorous ethical constraints” on weapons developed using AI.
As governments invest heavily in autonomous military technologies and AI-assisted defence systems, the “growing ease” of deploying them makes war more likely and “less subject to human control”, it warns. This “violates the principle that armed force should be used only as a last resort in cases of legitimate self-defense”.
The letter also criticises the growing concentration of technological power, and systems that reduce people to data or economic functions. It promotes what it calls a “civilisation of love”, centred on human dignity, solidarity, truth, compassion and the common good.
Pope Leo’s response to the the AI revolution deliberately references his predecessor Pope Leo XIII’s response to the problems of the Industrial Revolution, Rerum Novarum (“Of New Things”), in 1891. Though Magnifica Humanitas was released on May 25 2026, it is symbolically dated May 15, the date of Rerum Novarum.
Industrial Revolution to AI Revolution
An encyclical is not an ordinary papal statement. Traditionally addressed to bishops and the wider Catholic world, it is one of the Catholic church’s most authoritative teaching documents.
The pope no longer has the direct political power the papacy held in the 19th century. But papal teaching still carries moral weight across a global Catholic network of schools, universities, charities, hospitals and community organisations.
The Vatican cannot regulate AI. It cannot write safety standards, police data centres, or force companies to disclose how their systems work. But it can help shape the moral terms of the debate. For more than a century, Catholic social teaching has influenced public arguments about work, inequality, poverty, human dignity and the ethical limits of economic power.
Although popes issued encyclicals long before the modern era, Rerum Novarum made social encyclicals globally influential.
It confronted exploitative labour conditions, widening inequality, and conflict between workers and employers. Pope Leo XIII defended workers’ rights and argued that wealth carried social responsibilities. He criticised both unrestricted capitalism and revolutionary socialism.
The document influenced debates about labour rights and economic justice well beyond the church. In Australia in 1907, Justice H.B. Higgins drew on Rerum Novarum when establishing principles for a fair living wage.
Pope Leo XIV’s encyclical attempts to do for the AI age what Rerum Novarum did for the industrial age: provide a moral framework for a technological transformation reshaping work, power and human relationships.
Human dignity in the age of algorithms
Pope Leo XIV argues human rights are not granted by governments or corporations: they arise from the intrinsic dignity of every person. Technologies should serve humanity rather than reduce people to data, economic units or optimisation problems.
He builds on Pope Francis’ critique of “the tendency to let the logic of efficiency, control and profit alone shape personal, social and economic decisions”, in his 2015 encyclical. It, too, warned of the risks of technology.
Pope Leo XIV argues moral responsibility can’t be transferred to automated systems, regardless of how sophisticated they become. He also rejects transhumanist ideas that human limitations should be technologically overcome, arguing vulnerability, dependence and imperfection are essential to being human. Relationships, care, solidarity and compassion are not weaknesses. “Humanity flourishes not despite limitations, but often through them.”
Running throughout the encyclical is a contrast between a “culture of power” and a “civilization of love”. One treats technology primarily as a tool for domination and control. The other places human dignity, justice and care at the centre of social life.
Why this matters
The significance of Magnifica Humanitas lies in its ability to shape public conversation and moral imagination. Moral frameworks matter. They influence what societies fear, what they tolerate, what they defend – and what they refuse to sacrifice.
Governments are investing in AI capability while still developing frameworks for transparency, accountability and safe deployment. Businesses are adopting AI tools at speed. Schools and universities are rethinking assessment, authorship and learning. Workers are being asked to adapt to systems they did not design and often cannot challenge. And citizens are increasingly governed, assessed and targeted by automated systems they may never see.
Pope Leo XIV’s intervention reminds us the central question is not whether AI will be powerful: it already is. The question is whether that power will be made answerable to human dignity.
The future of AI will not just be decided in laboratories, boardrooms or parliaments. It will also be decided by the moral limits societies are willing to set. Pope Leo XIV’s encyclical is an attempt to draw those limits.![]()
Niusha Shafiabady, Professor in Computational Intelligence, Australian Catholic University; Darius von Guttner Sporzynski, Professor of History, Australian Catholic University, and Sandie Cornish, Senior Lecturer, Theology, Australian Catholic University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Reviewed by Irfan Ahmad.
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• Governments May Shape What AI Chatbots Say by Shaping the Data They Learn From
• Google AI Overviews Misinfo Problem on “Caliph” Answers Persists for Years, Showing Ongoing AI Search Failure
• YouTube’s Ad-blocker Crackdown Has Users Searching For Alternatives
by External Contributor via Digital Information World
Monday, May 25, 2026
YouTube’s Ad-blocker Crackdown Has Users Searching For Alternatives
YouTube adopted its premium model in 2018, alongside its YouTube Music subscription service. This has led to more ad-supported content on the platform, which has increased over the years. Many users, for the better part of the first 5 years of the service, had been able to use ad-blocking software to skirt the advertisements and still watch their favorite online content.
That changed in 2023 when YouTube started cracking down hard on ads in an attempt to get more users to subscribe to their premium service. The platform began using pop-ups warning users that ad-blocking software violated the site’s terms of service and would prevent users from watching videos until they either set their ad blocker to allow ads on YouTube or fully uninstall the blocker.
This change upset users, and they were in search of an alternative. The digital security site All About Cookies noticed a change in users' online behavior since then.
A Huge spike in Ad-block interest
According to their analysis, there was a +336% increase in traffic for users searching for an ad-blocker that can bypass YouTube’s restrictions following the YouTube crackdown in October of 2023.With the timing of this surge positioned right at the beginning of the ad crackdown, it’s highly likely that YouTube’s stance on ads changing was a direct result of this traffic, showing that users felt very strongly about the changes being implemented on the platform.
Most Users Would Not Pay for Ad-Free YouTube
Another indication of users’ strong negative feelings about the changes YouTube has made regarding ads can be seen in the survey, All About Cookies, conducted. In this survey, they asked users if they would pay for an ad-free version of YouTube known as YouTube Premium, 52% of whom said they would not, even with a heightened emphasis on ads.Another surprising result is that 75% of users would be willing to pay the actual cost of the lowest tier of ad-free YouTube. YouTube Premium Lite, which includes ad-free viewing on non-music and shorts content, the ability to play videos in the background, and the ability to download videos offline, is $8.99. This does not give you the full ad-free premium experience on YouTube, as that costs $15.99.
According to this data, only 11% of users would be willing to pay what it costs for a full ad-free YouTube experience.
Over Half of Users Plan to Look for Alternatives or Find a Way Around YouTube’s Crackdown
Another question revolved around asking users how the crackdown changed the way they used the platform. The results showed that a majority of respondents planned to find alternatives or ways around the ad-blocker crackdown, as opposed to YouTube’s intended response of paying for a premium subscription.While 23% users responded and said they would be less likely to use an ad-blocker or more likely to pay for a premium subscription, 53% of respondents plan to use their time to be spiteful and find a way around YouTube’s ad-block ban, look for alternatives, or straight up spend less time on the site.
44% of respondents said this crackdown would not affect their YouTube consumption.
It surely seems that most users’ response to YouTube’s attempt to get more users to watch ads or pay up was not met with the desired response. While around half of users do plan to adhere to the ban, a majority are looking the other way when it comes to watching content online.
More users (16%) claimed they are planning to spend less time on the site than users who said they were more likely to pay for YouTube Premium (12%).
Final Thoughts
This data shows that YouTube’s crackdown on ad blockers still has users upset with the changes they implemented in 2023. A huge increase in traffic on a site such as All About Cookies, searching for alternatives to ad-blockers not banned by YouTube, matches with a user survey where a majority claim they will not adhere to the site's demands of “watch our ads or pay up.”YouTube has gone the way of traditional streaming services and created a subscription-based way to watch content online, which many users feel is unfair because YouTube is primarily defined by user-generated content. The site has undoubtedly evolved since its inception over 20 years ago, with many controversial changes, such as introducing ads in the first place. However, it seems that this change has angered users more than ever, as they potentially look for new alternatives or ways around the change in strategy. Even three years after the initial crackdown, users are still scorned by the platform, and with new price hikes for premium subscriptions, many more users who were willing to pay may not be anymore
Author Info: Derick Migliacci is a Digital PR Strategist for AllAboutCookies. He brings over 3 years of experience in the PR world as well as a passion for digital trends, cybersecurity, and technology.
Reviewed by Irfan Ahmad.
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• Google’s AI Search Has Struggled With One Religious Question for Years
• No one trusts a chatbot that forgets them: Why healthcare AI keeps losing the people it's trying to help
by Guest Contributor via Digital Information World
No one trusts a chatbot that forgets them: Why healthcare AI keeps losing the people it's trying to help
Most people think of AI in healthcare as basic, rule-based tools, the kind that asks you to rate your pain from one to ten, reminds you to take your medication, and nudges you to book a follow-up appointment. It works, to a point. But if you've ever tried to sustain meaningful change in your own behaviour, whether that's managing a long-term condition, building an exercise habit, or finding a way to talk about mental health, you'll know that life doesn't follow a decision tree.
Healthcare has become very good at scaling systems that are reliable, auditable, and clinically consistent. What it has struggled to scale is sustained support. Most health interventions still rely on moments like appointments, check-ins, assessments, reminders. But behaviour change doesn’t happen in a moment.
Much of today’s healthcare AI is built around structured pathways and predefined responses. In many clinical contexts, that predictability is exactly what you want. Dosing calculators, diagnostic decision support, and triage systems all benefit from consistency and explainability. Many also incorporate sophisticated personalisation models built on prior interactions and user data.
But managing a chronic condition is not a linear process. Someone living with Type 2 diabetes might engage consistently for weeks, then disappear entirely for a month. They might understand exactly what they should do and still struggle to do it. Systems that respond with repetitive prompts or generic advice quickly lose credibility.
Generative and non-deterministic AI changes the shape of what healthcare support can look like because it allows systems to respond dynamically to what someone is saying, how they are engaging, and what appears to be helping. The user is not progressing through a predefined flow, they are having a sustained conversation over time.
That does not automatically make these systems useful. Large language models can still produce vague, generic, or misleading outputs if they are poorly designed or insufficiently grounded in clinical expertise and behavioural science. Combining conversational flexibility with specialist knowledge, clinical safeguards, and exceptional design is vital.
The case for conversations that remember
A 2024 review of over half a million health app users found that 70% had abandoned their app within the first 100 days. The pattern is consistent with what behavior change research has long shown: sustainable change happens through sustained engagement. Change is built through the accumulation of many interactions over time, each one building trust, slightly recalibrating the relationship between the person and the system supporting them.Non-deterministic AI enables that continuity. It remembers previous conversations, recognises patterns, and adapts to changing circumstances without forcing people to repeatedly restate themselves. The outputs are shaped deliberately by the conversation and by the frameworks built into the system's design.
Generative systems can enable the kind of conversational variation and contextual continuity that more closely mirrors human coaching. The feeling of being known rather than processed, begins to shape behaviour in ways a static system never could. People are more likely to engage honestly when support feels relevant to their situation.
Capability means nothing without access
One of the biggest practical problems in digital healthcare is friction. Many systems assume users will download an app, create an account, learn a new interface, and consistently return to it over time. For people managing chronic conditions, older adults, or communities with lower digital literacy, that process alone becomes a barrier.So why do these conversations have to happen inside an app at all? SMS remains one of the most accessible communication channels available. People already know how to use it. There is no onboarding process and no interface learning curve. Delivering conversational AI through SMS lowers the threshold for engagement, particularly for groups often underserved by more complex healthcare systems.
Delivering non-deterministic AI through SMS requires careful development. Without visual interfaces or navigational cues, the conversation itself has to carry the full weight of the interaction. Generative capability alone does not guarantee meaningful support. A non-deterministic system, like a large language model (LLM), can still produce irrelevant, confusing, or outright generic responses if not properly constrained or if the underlying model is of poor quality. Embedding expertise from coaches and healthcare practitioners is what enables these models to make a medical difference.
Image: Vitaly Gariev / unsplash
We’ve recently seen these concerns alleviated with responsive work for RVO Health. Conversational AI coaching delivered via SMS is being used to support behavior change at scale. Early signals around engagement and retention suggest that removing friction from the access point, while investing in the quality of the conversation itself, keeps people in it longer. That sustained presence matters in the moments existing care systems were never built to reach.
Filling the gaps human care can't reach
Behavior change is rarely decided in a clinic. It happens at three in the morning, on the way to the fridge, in the quiet space between a hard day and a good intention. Clinicians, nurses, therapists, and coaches cannot be present in those moments, and existing care systems aren't resourced to reach them. That's the gap context-aware conversational systems can fill, extending care where existing care systems are least able to reach.Healthcare challenges most dependent on long-term habits, obesity, diabetes, mental health, medication adherence, have historically resisted scale because scaling support has usually meant making it less personal. Systems that feel generic lose engagement quickly. Systems that adapt over time have a better chance of maintaining trust.
The science behind behaviour change is not new. What is changing is the technology’s ability to deliver more adaptive forms of support at scale. Generative AI makes it possible to combine conversational interaction with behavioural science, clinical safeguards, and specialist knowledge in ways previous digital health systems struggled to achieve. For the first time, the technology exists to deliver it. The work now is making sure we build it well enough to deserve the trust.
Reviewed by Irfan Ahmad.
Read next: Governments May Shape What AI Chatbots Say by Shaping the Data They Learn From
by Guest Contributor via Digital Information World
Friday, May 22, 2026
Governments May Shape What AI Chatbots Say by Shaping the Data They Learn From
Six studies show how state-coordinated media in AI training data influence model responses about politics—especially in a country’s own language.
Image: AI-generated by DIW for illustrative purposes.
Ask an AI model the same political question in two different languages, and you may get two very different responses. A new study in Nature suggests one reason why: governments can indirectly influence large language models (LLMs) by shaping the online media environment, and thus the text those systems learn from.
A cross-university team of New York University researchers and their colleagues found evidence that state media control can leave detectable traces in AI model behavior. The researchers combine evidence from evaluating LLMs in the local languages of 37 countries with a case study from China to understand how this happens. Across six studies, the team traced the pathway from online media to training data to model behavior, combining analysis of open training data, experiments with training small models, human evaluation, and real-world tests of commercial chatbots.
“People often talk about AI as if it learns from the internet in some neutral way,” says Hannah Waight, co-first author of the study, an assistant professor of sociology at University of Oregon, and a former postdoc at NYU’s Center for Social Media, AI, and Politics (CSMaP). “It doesn’t. It learns from information environments that have already been shaped by institutions and power, and those environments can leave measurable traces in what models say.”
The researchers call this idea institutional influence.
“The public debate has focused on what AI can generate, but this study points upstream. Before AI systems can influence politics, politics can influence AI,” observes Joshua A. Tucker, a co-author of the paper and co-director of NYU’s CSMaP. “This is a democracy and governance issue, not just a technical issue. As people turn to chatbots for political information, we need to examine which institutions have shaped the answers before a user ever asks the question.”
“We spent years studying how political information flows through traditional and social media. Now we need to do the same thing for frontier models,” adds Solomon Messing, a paper co-author and research associate professor at CSMaP. “The challenge is that in AI systems, those flows are obscure, because the origins of the data become difficult to trace once information is absorbed during model training.”
The study also included researchers from Purdue University, the University of California San Diego, and Princeton University.
To trace institutional influence through the training process, the authors first showed that state-coordinated media appears frequently in real training data. Comparing two sources of Chinese state-coordinated media with a major open-source multilingual training dataset derived from Common Crawl—a nonprofit that provides web-crawl data—the researchers found more than 3.1 million Chinese-language documents with substantial phrasing overlap, or about 1.64 percent of the dataset’s Chinese-language subset. That is over 40 times the rate for documents from Chinese-language Wikipedia, a common training source. Among documents mentioning Chinese political leaders or institutions, the share rose as high as 23 percent. Only about 12 percent of the matched documents came from known government or news domains, suggesting that the material had spread widely across the web before reaching AI training corpora.
The researchers also found that commercial models memorized distinctive phrases associated with this material, suggesting that they had been seen a number of times during training.
“State-coordinated content is not just about what appears in official media,” says Brandon M. Stewart, one of the paper’s authors and associate professor of sociology at Princeton University. “It is also about recirculation; the same phrasing moving through newspapers, apps, reposts, and ordinary web pages until it looks like part of the broader information environment. Once state-coordinated content is in the training data, the model can launder it into what looks and sounds like neutral, objective information.”
The team then tested whether that content could actually shift a model’s behavior. Large commercial models take months and millions of dollars in compute to train so the team experimented with taking a small, open model and adding additional documents to the training process.
The results were clear: adding scripted news to the training data made the models more likely to produce more favorable answers—nearly 80 percent of the time compared with an unmodified model. This is true even when compared to other non-scripted Chinese media, and especially compared to just adding general Chinese-language text from the internet.
“When the same political question produces systematically different answers with only small changes to the training data, that suggests those additional documents are doing real work,” explains Eddie Yang, co-first author of the study and assistant professor of political science at Purdue University.
The team reasoned that if states have strong real-world influence over the pretraining data, it should appear most clearly in the state’s primary language. For example, a question about the Chinese government should produce a more pro-government answer when posed in Chinese than the same question posed in English.
They used this within-model, cross-language comparison to probe commercial models without access to their internal parameters. In responses to political questions about China, human raters judged the Chinese-prompted answer to be more favorable to China 75.3 percent of the time. For prompts not about China, the rate was no different from chance. The language difference gave them a rare window into a closed system. Follow-on studies using real user prompts and additional commercial models found the same general tendency: on questions about Chinese leaders and institutions, answers tended to be more favorable when the prompt was in Chinese than when it was in English.
Additionally, in a cross-national study of 37 countries where a national language is largely concentrated within a single country, models portrayed governments and institutions from countries with stronger media control more favorably in that country’s language than in English. The authors emphasize that this result is correlational, but say it is consistent with the mechanism identified in the China case study.
“This is not evidence that AI companies set out to curry favor with those governments, or that those governments control media systems with chatbots in mind,” says Margaret E. Roberts, a co-author and professor of political science at UC San Diego. “States shape the information environment, the information environment shapes training data, and training data shapes model outputs. But going forward, our findings suggest that LLMs create new incentives for powerful actors to think strategically about the text they disseminate online.”
The authors stress that no single test can capture how a commercial model was trained because many of those details aren’t publicly known. The paper instead combines multiple approaches including analysis of open-source data, memorization tests of commercial systems, retraining experiments, human evaluation, real-user audits, and cross-national comparison to identify one of the ways that political power can enter AI systems. At their project website, the authors show that the results replicate using the latest models released.
Beyond nation-states, the researchers emphasize that other powerful institutions may also be able to shape large volumes of online text.
“Training data is the foundation of modern AI,” says Messing. “If we want to understand the powerful interests these models reflect, we need to know how we’re sourcing the concrete. That starts with more transparency about what goes into the training data.”
This post was originally published on New York University News and republished here with permission.
Reviewed by Irfan Ahmad.
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by External Contributor via Digital Information World
Taunting and degrading civilians in armed conflict is a clear violation of international law
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
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.
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by External Contributor via Digital Information World
Thursday, May 21, 2026
Technology usually creates jobs for young, skilled workers. Will AI do the same?
A new study of the postwar U.S. shows which kinds of workers historically filled new tech-enabled jobs.
Image: Vitaly Gariev - unsplash
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.
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