5 ways data centers endanger their local communities and the country as a whole

Neha Gour, George Mason University; Ed Maibach, George Mason University, and Luis Ortiz, George Mason University

Image Caption: Hosted by the Stanford Research Computing Facility (SRCF) on the SLAC campus, the U.S. Data Facility is the main hub of Rubin's data infrastructure. Credit: Olivier Bonin/SLAC National Accelerator Laboratory / NOIRLab. Licensed under a Creative Commons Attribution 4.0 International License.

Every internet search, streamed video and AI-generated response depends on a data center somewhere. Driven by rapid growth in artificial intelligence, cloud computing and cryptocurrency, data centers have become the backbone of the modern digital economy. But though their key role is in enabling virtual and remote experiences, data centers are physical buildings in real communities around the nation and the globe.

The United States hosts more than 4,000 data centers – more than any other country. The U.S. Department of Energy expects that, taken together, all U.S. data centers will consume as much as 12% of all U.S. electricity by 2028. In 2023, data centers consumed about 4.4% of total U.S. electricity – roughly 176 terawatt-hours.

In the U.S., Virginia has more data centers than any other state – over 600, two-thirds of which are in the northern Virginia suburbs of Washington, D.C. In 2023, the state’s data centers consumed about 26% of Virginia’s total electricity supply – a higher share than in any other state.

We study science communication, climate science and public health, so we wanted to understand how data centers in Virginia affect the people who live near them and the broader public.

We found that the data centers that already exist affect nearby residents and the nation as a whole in five main areas: air quality, water quality, noise levels, land use and energy costs.

Air pollution

Data centers generally operate 24/7 and consume enormous amounts of electricity, which must be generated somewhere – either near the data center or farther away.

When fossil fuels are burned to generate that power, they emit a wide range of air pollutants, including those linked to lung disease, cardiovascular disease, stroke and neurological conditions. They also emit heat-trapping pollution that causes global warming and climate change, which, in turn, worsens air pollution further.

Generating power for U.S. data centers in 2023 emitted the equivalent of 2.2% of the nation’s greenhouse gas emissions. Other air pollutants emitted from fossil-fuel combustion are associated with increased risk of ADHD and autism in children and risks of Parkinson’s and Alzheimer’s diseases in older adults.

Unless the energy powering data centers comes from clean energy sources, such as solar, wind or geothermal, generating that electricity also pollutes the air. People who live near fossil-fuel burning power plants, whether in communities that also host data centers or in distant states, are exposed to air pollution. And during electrical outages, on-site diesel generators kick in, releasing large amounts of air pollution that can harm data center employees and nearby residents alike.

Water consumption and pollution

Data centers require vast quantities of water to cool their servers. Globally, they are projected to consume between 4.2 billion and 6.6 billion cubic meters of water annually by 2027. In the United States, data centers already rank among the top 10 industrial water users.

In northern Virginia, data center water use has risen sharply. In Loudoun County alone, just northwest of D.C., potable water use by data centers more than doubled between 2019 and 2023, while facilities across northern Virginia consumed nearly 2 billion gallons of water in 2023.

This demand can strain local rivers, aquifers and municipal water systems, even in regions like the mid-Atlantic that are not usually prone to drought, but especially in regions like the U.S. Southwest that face persistent droughts.

Noise pollution

Data centers’ continuous operation means that cooling systems, including air chillers and cooling fans, generate a persistent humming sound around the clock – as do any generators that are in use to provide power.

In northern Virginia, some residents have complained about an industrial-scale “drone” or “hum.” Measurements at the data centers that were the subject of complaints found noise levels were between 40 and 59 decibels on residential property.

Those noise levels are quieter than a conversation with someone 3 feet away and not loud enough to damage people’s hearing or violate local noise ordinances. But they are close to levels the EPA says reduce people’s ability to work, sleep and exercise. Some people have complained that data center noise has given them trouble sleeping and concentrating, and some have said they avoid using their homes’ outdoor spaces, where the noise is louder.

Land use and community well-being

Data center expansion often targets land near green spaces, agricultural areas or rural communities where developers can secure affordable land with access to existing electricity supplies.

Converting green space into industrial facilities can diminish health benefits associated with being in and near natural environments, including opportunities for physical activity and improved mental well-being.

In Virginia, residents living near data center construction have reported increased exposure to truck traffic and diesel exhaust, which can contribute to respiratory and cardiovascular health risks, especially in children and older adults. While these effects are typical of large construction projects, they can be amplified when several data centers are clustered together.

In places like Prince William County, Virginia, developers have proposed data centers on roughly 2,400 acres of undeveloped land in the Rural Crescent, an area designated by the county’s planners to remain relatively undeveloped. Those data centers could transform open space and rural farmland into industrial zones, disrupting communities with long-standing ties to the land.

Rising energy costs

As data centers increase electricity demand, they put upward pressure on energy prices across the grid. A 2024 Virginia legislative report found that the state’s typical residential electricity bill could rise by $14 to $37 per month by 2040 because of grid strain tied to data center growth – a 9% to 25% increase over current average bills, and a figure that does not factor in potential inflation.

These higher costs are paid by all consumers, but they place a greater burden on families that are most economically distressed, who also tend to have more health problems. Lower-income families spend a higher share of their budget on electricity, and when bills rise, the consequences can include reduced access to adequate heating and cooling, increased risks of heat-related illness and cold-related cardiovascular stress, as well as difficult choices between paying for energy and food or healthcare.

What can be done

Many of these health harms can be mitigated through better planning and design.

Increasing the share of renewable energy used to power data centers would help reduce air pollution and associated health harms.

Using recycled water in targeted systems that cool individual server rows or racks rather than whole buildings can significantly reduce cooling energy demand, with some studies estimating reductions of up to 29%.

On noise, a Leesburg, Virginia, data center reduced low-frequency tonal noise by reengineering its fan mounts.

And on energy costs, requiring large-scale data centers to cover more of the grid costs they create could help protect residential customers from higher electricity bills.

The world’s digital infrastructure runs through data centers, and that is not changing. We believe that expanding this infrastructure without protecting the health of surrounding communities is an unacceptable option.

Neha Gour, Ph.D. Candidate in Science Communication, George Mason University; Ed Maibach, Distinguished University Professor Emeritus of Communication, George Mason University, and Luis Ortiz, Assistant Professor of Atmospheric, Oceanic and Earth Sciences, George Mason University

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

Reviewed by Irfan Ahmad.

Read next: Is Regulation Holding Back Tech in the EU?

by External Contributor via Digital Information World

Is Regulation Holding Back Tech in the EU?

By Felix Richter, Data Journalist, Statista

Two years after unveiling Apple Intelligence at WWDC 2024, Apple’s most wanted AI feature – a smarter and more capable version of its digital assistant Siri – is finally around the corner. Unless you live in the EU, that is.

On Monday, Apple officially announced Siri AI, an “entirely new version of Siri,” which Apple describes as “profoundly more capable and personal.” Originally planned to be launched alongside Apple Intelligence in late 2024, the AI-powered Siri was marred with problems, delayed several times and ultimately rebuilt from the ground up. The new Siri will be available as a beta to English-language users later this year, with one notable exception: the European Union.

As Apple announced in a separate press release, Siri AI will not be coming to the EU for the foreseeable future due to the Digital Markets Act’s interoperability rules, which would require Apple to grant other virtual assistants a degree of system-level access, which the company deems unsafe and unacceptable. It’s not the first time that Apple has held back a release in the EU to ensure compliance with EU rules and, judging by the strongly worded press release, the company is not happy about it.

The first suite of Apple Intelligence features, released in the U.S. in late 2024, was also delayed several months in the EU and other companies have encountered similar problems when trying to comply with EU laws. Back in 2023, when Meta launched Threads, a text-based social media app linked with Instagram, the EU was also excluded due to GDPR and DMA-related concerns. Back then, it was impossible to sign up to Threads without linking an Instagram account, a practice likely non-compliant with EU rules. It took several months for those issues to be resolved and, when Threads finally launched in the EU in December 2023, users had the option to create an account from scratch.

In recent years, the EU has made a name for itself by repeatedly going toe-to-toe with U.S. tech on issues ranging from tax evasion to privacy laws and antitrust rules. And while this shows that even the most powerful companies in the world must comply with the rules, it has also created a situation where EU users are left behind increasingly often when it comes to the latest tech or digital services. This could create backlash against regulation, which is why it’s important that the EU demonstrates its will to cooperate with tech companies to find solutions, as it has in the past.

This post was originally published on Statista and republished here with permission.

Reviewed by Irfan Ahmad.

Read next:

• Users trust AI and human fact-checkers equally, but for different reasons

• New research from a multi-institution consortium finds major AI models often omit religious perspectives in responses
by External Contributor via Digital Information World

Users trust AI and human fact-checkers equally, but for different reasons

By Jonathan F. McVerry, The Pennsylvania State University

Image: AI-assisted, generated for illustrative purposes by DIW

Users tend to trust artificial intelligence (AI)-powered fact-checkers as much as human fact-checkers, but for different reasons, according to a new study led by Penn State researchers. The researchers said there is no definitive “winner” when comparing the two fact-checking systems, because users see distinct strengths and weaknesses in each.

In their study published in Media Psychology, participants tended to trust AI more for large-scale scanning tasks, like identifying “red flags” in social media posts. They trusted humans for more nuanced fact-checking that requires piecing together evidence or interpreting complicated situations.

“There's a very clear distinction that emerges from the study that AI is considered good at low-level linguistic features, like identifying telltale signs that something is not credible,” said author S. Shyam Sundar, Evan Pugh University Professor and James P. Jimirro Professor of Media Effects at Penn State. “Humans are seen as being better at corroborating evidence from multiple sources.”

The research team first conducted a pretest to identify six news headlines that varied in credibility. Two hundred and ninety-one participants residing in the United States were then shown those headlines in simulated social media posts via an application created for this study called FactDeck. Some posts were labeled as fact-checked by an AI system and others by human fact-checkers. Participants saw one of three types of explanations:

• Evidence-based: The system labeled the post false with a reference to the information that contradicted the post.
• Feature-based: The system flagged suspicious wording or unusual phrasing.
• “Black box”: No explanation was given for why the post was marked false.

The researchers focused on “machine heuristics” — mental shortcuts people use when evaluating AI, based on stereotypes about machines. They found that while participants assumed AI systems were objective and accurate, they also distrusted AI for lacking human judgment. When it came to determining which system garnered the most trust, first author Mengqi Liao said the two opposite perspectives offset each other.

“Some studies only compare AI versus human fact-checkers, to find out which is trusted more,” said Liao, assistant professor at the University of Georgia, who completed her doctoral studies with Sundar at Penn State. “They get a lot of inconsistent results. That’s why we proposed a competing hypothesis that showed how positive and negative views of both can coexist and cancel each other out.”

Liao added that users preferred some explanation rather than none — the “black box” option.

“We want to provide enough explanation to users that helps them better understand how the system makes a specific decision. It may help them calibrate their trust,” Liao said. “They're not just relying on the system’s decision. They can also make a judgment based on how the system reached the decision.”

The findings suggest that effective fact-checking tools should not only provide accurate results but also explain how those results are reached. Liao said that fact-checking programs should help people recognize what AI systems are good at and what they're not, rather than relying on users’ own naïve, outdated notions of machine capabilities.

Sundar said this is increasingly important as AI fact-checking becomes more necessary. Human fact-checking can’t keep up with the volume of misinformation on social media today.

“The ideal situation would be a human-AI collaboration, but it's not always possible for humans to intervene and check for evidence from multiple sources,” Sundar said. “So, we are going to have to, at some level, completely automate this whole fact-checking business, and rely on AI, which can be much better at efficiently sifting through evidence from multiple sources than humans, despite what people think.”

Sian Lee, assistant professor at the University of Mississippi, who earned a doctorate in informatics from Penn State in 2024; Annie Dooley, a doctoral student at the Ohio State University, who earned a master’s degree in media studies at Penn State; and Aiping Xiong, assistant professor of privacy and cybersecurity informatics at Penn State, were authors on the paper as well.

This post was originally published on Penn State News and republished here with permission.

Reviewed by Irfan Ahmad.

Read next: New research from a multi-institution consortium finds major AI models often omit religious perspectives in responses
by External Contributor via Digital Information World

New research from a multi-institution consortium finds major AI models often omit religious perspectives in responses

By Todd Hollingshead, BYU News

A new multi-university academic consortium led by Brigham Young University has found AI models have significant biases and gaps when it comes to addressing faith and religion.

Image: Kabila Haile - unsplash

Newly published research from The Consortium for Evaluation of Faith and Ethics in AI (CEFE-AI) — a collaboration among researchers at BYU, Baylor University, the University of Notre Dame and Yeshiva University — found a consistent, repeatable pattern: religious perspectives are being left out of AI responses.

“There are very practical questions people have about life, everyday situations about grief, love, loss, morality, and often AI does not bring religion into those conversations,” said lead researcher David Wingate, a BYU professor of computer science. “Religion is an important part of human flourishing; 75% of the world’s populations maintains religious identity. As we build AI technologies, there’s no reason we shouldn’t build them to support people in what’s important to them.”

CEFE-AI, which has posted three papers to date on AI’s religious bias and exclusion of religious topics, was announced May 26 at the Summit on AI Ethics in Athens, Greece. Elder Gerrit W. Gong of the Quorum of the Twelve Apostles of The Church of Jesus Christ of Latter-day Saints gave the keynote address, emphasizing the need to portray faith traditions accurately, honestly and respectfully.

“The world’s great religious, philosophical and ethical traditions have guided human civilization and society for millennia; we need that wisdom and those values to anchor AI today,” Elder Gong said. “To offer all it can for the greater good of individuals and society, AI needs to reflect faith, moral compass, and the gift of possibility.”

As key part of their work, CEFE-AI has released initial datasets of the AllFaith Benchmark, one of the first multi-faith sets of tests that examines how AI systems engage with a plurality of religions. The benchmark includes hundreds of real-world ethical questions sourced from ChatGPT transcripts and faith-community contributors. The researchers have tested the benchmark on 14 different LLMs, including flagship models from Anthropic (Claude 4.7), Google (Gemini 3.1), xAI (Grok 4.2) and OpenAI (ChatGPT 5.5). Key findings include:

• A survey of 1,125 Americans found most people expect religious perspectives in responses to ethics questions, but nearly all AI models failed to provide any religious content in answering those queries.
  • “Consistent with studies that show religion's persistent moral relevance for the majority of the world's population, we also found that people see religion as significant across hundreds of real-world ethical questions,” said Paul Martens, professor of ethics at Baylor University. “Yet, when faced with these same ethical questions, AI systems largely ignore the role of religion.”
• Models show clear and consistent biases in giving guidance about religion conversion, systematically encouraging movement toward some faiths and away from others.
• In over 12,000 research papers about AI bias, only 0.2% address religious bias

“More than any previous technology, AI influences public discourse and perceptions. When AI actively excludes religious voices from these important conversations, it impoverishes humanity, rather than enriching it,” said Fr. John Paul Kimes of the University of Notre Dame. “The exclusion of faith from the digital public square diminishes our capacity for authentic dialogue which is necessary to build up the common good.”

The researchers also used the AllFaith Benchmark for a conversion bias test and found that models would subtly encourage users toward conversation to some faiths, while subtly discouraging users from converting to others.

Across all models, the biases were consistent and measurable:

• Nearly every model produced a negative bias towards Jehovah’s Witnesses and a positive bias towards Catholicism.
• Models from Anthropic and Meta showed the least bias of any models tested.
• Grok produced the strongest biases — strongly favoring Catholics and Protestants, while showing negative bias toward Jehovah’s Witnesses, Baha’i and Hindus.

CEFE-AI representatives said the group is just at the beginning of their research partnership. They hope their continued work makes it to the eyes of language model providers, leading to constructive conversations of how to improve their products to better benefit humanity.

“AI is changing the world at an astounding rate, with implications in every area of life,” said Rabbi Daniel Feldman of Yeshiva University. “It is crucial that those who care about the role of religious values in the world engage proactively with those driving these changes so that we continue to see these values reflected and honored in the new landscape.”

Reviewed by Irfan Ahmad.

This post was originally published on Brigham Young University News and republished on DIW with permission. The headline has been updated for clarity.

Read next: 

• If AI is addictive, where does the responsibility lie – with big tech or its users?

• Top search engines’ AI systems have struggled with one Islamic caliph religious question for years

• Inside Kusto Group's 2026 Agenda: What Yerkin Tatishev Is Building Next

by External Contributor via Digital Information World

Inside Kusto Group's 2026 Agenda: What Yerkin Tatishev Is Building Next [Ad]

If you want to understand where a company is headed, don't read the press releases. Look at where the money is going, what's under construction, and which new entities quietly appeared in the business registry this spring. Do that with Kusto Group right now, and a clear picture starts to emerge — one that's more ambitious than anything the company has attempted before.

Yerkin Tatishev founded Kusto Group in 2002 and has spent over two decades building it into a diversified industrial holding with operations across nine countries. But 2026 feels different. The projects currently in motion aren't incremental. They're the kind of bets you make when you've spent years laying groundwork and finally feel ready to put it all together.

The Logistics Complex Nobody Is Talking About Enough

Start with what's happening in the Almaty region, because it matters more than it might look at first glance.

Kusto Group is launching a logistics complex worth 28 billion tenge. That's a real number — not a concept, not a feasibility study. Ground is broken, capital is committed, and the facility is designed specifically to handle the scale of agricultural and commercial volumes that Kusto's operations are now generating.

Here's why it matters beyond Kusto Group itself: Kazakhstan has been pushing hard to become a genuine export hub, not just a country that grows grain and ships it raw. That requires infrastructure — cold storage, distribution networks, transport coordination — and that infrastructure is exactly what this complex is built to provide. The timing isn't accidental. Record grain export volumes came out of 2025. The question for 2026 is whether the physical capacity exists to do it again, at greater volume, more reliably. This complex is the answer to that question.

It's the kind of project that doesn't generate much noise but ends up being foundational to everything that follows.

Image: Ethan Wilkinson, Unsplash

Kusto Group Is Building Its Own Transport Network

This is where things get genuinely interesting.

In April 2026, two new companies appeared in the registry. Nomad Tankers Ltd — sea and coastal cargo transport. JanaPort Holdings Ltd — airport management and cargo air transport. And sitting alongside those: a cargo airline project with a $30 million budget.

Kusto Group is, in effect, building the logistics spine it needs to run its own supply chains without depending on third parties at the most critical points. If you're moving grain from Kazakhstan to Asian markets, processing construction materials across multiple countries, and running operations from Israel to Vietnam, the cost and fragility of relying on foreign carriers adds up fast.

The cargo airline in particular is being framed as a national infrastructure argument — reducing Kazakhstan's dependency on foreign carriers. That framing isn't just PR. It reflects something real: if you're serious about building an export-oriented industrial business in Central Asia, you eventually have to confront the transport problem head-on. Kusto Group is confronting it.

Diamond: The Almaty Project That Looks Different on Purpose

In Almaty's Bostandyk District, construction crews are currently building the Diamond residential project — 15 blocks, each nine stories tall, sitting across from the MEGA Alma-Ata shopping centre.

Nine stories might not sound dramatic. That's almost the point. Most developers in this part of Almaty are going straight to fifteen floors and above, squeezing as many units as possible onto each site. Kusto Home — the group's real estate arm — chose to go in the opposite direction. Lower density, better quality, a layout that's actually designed for the people living there rather than for the pro forma spreadsheet that justified the project.

Tatishev has talked about this approach consistently across Kusto Group's real estate work: the conviction that if you build something genuinely good, the returns follow. Diamond is the second major urban development from Kusto Home, and the early signs suggest it's landing well with buyers who've grown tired of the default approach to new Almaty housing.

Tambour Is Getting Ready for the Public Markets

Kusto Group acquired Tambour — an Israeli paint and construction materials company — in 2014. It was a turnaround bet at the time. Over a decade later, it's become something considerably more valuable, with Tambour's first international acquisition (Colorificio Zetagi in Italy) signalling that the company isn't staying Israel-only.

The target now is an IPO by 2028. Getting there requires serious preparation — governance structures, audited financials that meet international standards, a clean narrative for institutional investors. That groundwork is being laid through 2026, which means a lot of the behind-the-scenes work is happening right now, even if it's not visible from the outside.

When it lands, it'll likely be one of the larger public market moments in Kusto Group's history. The patient approach Tatishev applied to Tambour over eleven years is about to get its most public test.

Kazseeds and the Long Game on Agriculture

Most of Kusto Group's agricultural story in 2025 was about grain volumes and export records. The longer game is playing out through Kazseeds — the seed genetics venture the group launched in 2019 with Baumgartner Agricultural Science and Services.

The idea is straightforward: develop climate-resilient, high-performance seed varieties suited specifically to Central Asian growing conditions, and supply them to farmers who are facing increasingly unpredictable weather patterns. The global seed development market is heading toward $92 billion by 2028. Central Asia has both the agricultural land and the need for better seed technology.

Kazseeds isn't chasing that market from a standing start. It's been in development for six years, building scientific credibility and agronomic track record in the region. By the time the broader market growth arrives, the infrastructure — research, distribution, farmer relationships — should already be in place.

Georgia: Still Unfinished Business

The Telegraph Hotel opened in Tbilisi in June 2025 and immediately became Georgia's first property in the Leading Hotels of the World network. That's a meaningful achievement — but anyone who knows Tatishev's relationship with Georgia, which goes back twenty years, understands that the hotel is part of something larger, not a standalone project.

The Tsinandali Festival continues each September, now in its seventh edition, drawing world-class performers and young musicians from across the Caucasus and Central Asia to the historic Tsinandali Estate. The combination of a leading luxury hotel in Tbilisi's most important street and an internationally recognised cultural festival in the Kakheti wine region gives Kusto Group a platform in Georgia that very few foreign investors can claim.

What comes next in Georgia isn't fully announced yet. But after twenty years and two flagship institutions, it would be surprising if the answer were simply "nothing."

The Thread That Runs Through All of It

Looking across these projects — the logistics complex, the transport network, Diamond, the Tambour IPO, Kazseeds, Georgia — they're not a random collection of bets. They all point in the same direction.

Kusto Group under Yerkin Tatishev has always been a company that invests in the infrastructure that underlies other things. Not the flashiest layer. The load-bearing one. Grain storage that makes exports reliable. Transport networks that make supply chains autonomous. Residential developments that people actually want to live in. Seed genetics that farmers can depend on in a changing climate.

What's different in 2026 is the scale. Each of these projects is larger, more complex, and more consequential than what came before. The company has clearly reached a phase where it's ready to move beyond careful consolidation and into something that looks a lot more like acceleration.

Whether that reads as ambition or confidence probably depends on how well you know the two decades of work that got it here.

Editor's Note: This is sponsored content. The views expressed are those of the author and/or sponsor and do not necessarily reflect the views of this publication.

by Sponsored Content via Digital Information World

If AI is addictive, where does the responsibility lie – with big tech or its users?

Bernd Stahl, University of Nottingham

Image: Gus Tu Njana - Unsplash

When I talk to my son, an engineering student, and we have a question or disagreement, he immediately turns to ChatGPT as his primary source of information and confirmation.

He is not alone in this. The use of generative AI tools has exploded across different demographic groups. For many people, these tools can be entertaining, informative and beneficial. However, they also have a dark side.

Generative AI is not formally recognised as addictive right now – the medical evidence is still being gathered. But there is a significant amount of data showing heavy use of chatbots and other systems that produce text, images and video leads to neural patterns and behaviour that are associated with addiction.

In light of Meta’s and YouTube’s recent legal defeat in a landmark social media addiction trial, I believe it’s time to ask whether a similar logic applies to generative AI – and how it could be addressed. The starting point would be to identify who carries responsibility for overuse of generative AI.

The science on this is not settled, and there are some who counsel caution when using the term addiction. They propose the use of other expressions such as “problematic use”. However, in a recent paper, our team of researchers suggest there is strong evidence to suggest that generative AI has addictive properties.

Much-discussed examples include emotional dependency on chatbot companions, compulsive engagement with them, and the loss of real-world acquaintances and friends.

A key factor here is that, as in all cases of addiction, the behaviour has negative consequences for the user which may affect both their personal and professional lives.

If we follow the argument that generative AI is a candidate for addictive behaviour, then we also need to look at responsibility. Societies tend to find ways to deal with harm by holding people or groups responsible for fixing it. Those who could be accountable include legislators, regulators, industry and health systems.

Historical examples

Historical precedents such as smoking might offer insights into how the area of generative AI addiction could evolve.

Older readers may remember when the Marlboro Man would appear before any feature movie in their local cinemas. It eventually transpired that not only was smoking addictive and bad for your health, but that tobacco companies knew this. Nevertheless, it was publicly denied.

This led to lengthy and high-profile litigation, eventually resulting in large-scale financial payouts and changes to the industry. These changes included the plain packaging of tobacco products and gruesome warning labels on them.

Gambling could be following a similar trajectory – and now social media companies may be taking their first steps into a similar process.

A key question is whether the makers of a product – be it tobacco, gambling or social media – are aware of its addictive properties. Another important factor being considered is whether certain companies may even use the allegedly addictive properties of their products for corporate advantage.

AI is not tobacco, of course, but there may be parallels to be studied.

In our research, we have identified four groups of stakeholders that are now being called upon to address the challenges linked to the possibility of addiction to generative AI.

The first is governments and regulators. These have a key role to play in highlighting the problems, setting the rules of engagement, and creating incentives for other parties to engage with the topic.

They can do this by requiring labelling, restricting advertising, applying liability law and providing research funding – along with many other mechanisms.

But the most important role in addressing potential addictive behaviour associated with generative AI would be held by big tech companies that develop and own these technologies – and stand to benefit financially from them.

These companies own and have access to user data, which would be needed to ascertain the features that support or alleviate addiction. They are also the parties that would benefit financially from addiction by increasing user numbers and engagement, the main currency of the digital age.

In addition to these two groups, academic researchers have an important role in collecting and interpreting data, and providing the evidence needed to recognise addiction and addictive features – in ways that allow for evidence-based political or legal debate.

Finally, civil society organisations such as user or patient groups can help by providing support, advocating for members’ interests, and establishing early-warning structures.

The point is that none of these interested parties can address the problem on their own. They need to collaborate.

Someone else’s problem

A key problem at the moment is the lack of structured debate about responsibilities – everybody assumes it is someone else’s problem. But there is ample precedent showing how greater engagement from those involved with the issue may be achieved.

With tobacco, the World Health Organization (WHO) formed the Framework Convention on Tobacco Control – a treaty-based mechanism that brought together governments, public health bodies, researchers and civil society to evaluate evidence and draw up common rules. The International AI Safety Report shows comparable international consensus-building activities are already happening in other aspects of AI.

Some responsibility also falls on the users of AI, who should try to avoid or control their own potentially harmful behaviour. But appeals to individual moderation or mindfulness have been shown with other addictions to be insufficient.

While the harms associated with smoking or alcohol misuse are well known, society still relies on age limits, packaging rules and advertising restrictions. Generative AI is being integrated into the everyday fabric of our society. The choices we now make will determine what acceptable use looks like for years to come.

Bernd Stahl, Professor of Critical Research in Technology, School of Computer Science, University of Nottingham

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

Reviewed by Irfan Ahmad.

Read next: 

• Your Digital First Impression: What Does a Quick Search Reveal About You?

• How Artemis II livestreamed hi-def videos and images from the moon to Earth

by External Contributor via Digital Information World

How Artemis II livestreamed hi-def videos and images from the moon to Earth

By Ariana Gaines, MIT Lincoln Laboratory, MIT News

This first use of laser communications on a crewed mission at lunar distance was a foundational step to establishing a high-speed internet in deep space.

Image: NASA - Unsplash, Artemis II collection

This April, humanity had front-row seats to space as the Artemis II Orion spacecraft transmitted crystal-clear footage of its historic journey around the moon over more than 250,000 miles back to Earth at speeds on par with those of home internet connections.

The livestreaming of high-definition videos and high-resolution photos of the moon and Earth was made possible through the Orion Artemis II Optical Communications System (O2O). Developed by MIT Lincoln Laboratory in collaboration with NASA Goddard Space Flight Center, the onboard O2O payload was the space end of a high-speed laser communications (lasercom) link.

This link reached Earth when Orion had a line of sight with primary optical ground stations located at NASA’s White Sands Test Facility in New Mexico and Caltech/NASA Jet Propulsion Laboratory’s Table Mountain Facility in California, or an experimental ground station at Australian National University’s Mount Stromlo Observatory.

Together with terrestrial networks, O2O formed an internet backbone between the Artemis II Orion spacecraft and the Mission Control Center at NASA's Johnson Space Center in Texas.

Toward a high-speed internet in space

"Our goal was to demonstrate O2O's operational utility for human spaceflight, extending the high-bandwidth connections that internet users enjoy on Earth to astronauts in deep space," says lead systems engineer Farzana Khatri, a senior staff member in Lincoln Laboratory's Optical and Quantum Communications Group. "We not only demonstrated the first use of lasercom on a crewed mission beyond low Earth orbit, but also attracted massive public engagement as the astronauts shared multimedia from their journey in near-real time."

During the last missions to the moon in the late 1960s and early '70s, astronauts relied on radio-frequency systems to communicate. But radio waves can only carry so much data per second because of their low carrier frequency; the grainy, poor-quality video and images of the moon from that time speak to this limited bandwidth.

With its much higher carrier frequency, infrared laser light can transmit 10 to 100 times more data per second than can radio waves. The switch from Apollo-era radios to Artemis-era lasers is analogous to the move from dial-up to high-speed internet. And a high-speed internet is rapidly becoming a key requirement for NASA missions as they collect more high-resolution data and push humans farther into deep space.

Lasering in on unprecedented views

During the Artemis II mission, from April 1 to 11, O2O downlinked nearly half a terabyte of data at speeds up to 260 megabits per second. This data trove contained never-before-seen views of the basins and craters on the far side of the moon, a crescent Earth setting behind the moon, a nearly hour-long total solar eclipse with other planets scattered across a star-filled sky, and flashes of light from tiny meteoroids striking the lunar surface.

"O2O was able to downlink all the data stored on multiple onboard cameras, allowing mission control to erase the memory cards and refill them with new photos and videos," explains Khatri. "For any space mission, scientists and spacecraft engineers are concerned that data not sent down during the mission can become corrupted or get destroyed. And, when the spacecraft capsule returns, downloading the data can sometimes take months. The lasercom capability provided by O2O ensured the data were preserved and immediately available for analysis."

O2O is based on the laboratory's R&D 100 Award–winning Modular, Agile, and Scalable Optical Terminal (MAScOT), which contains subassembly modules for pointing the laser beams, establishing a communications link with ground stations, and maintaining this link despite atmospheric conditions. MAScOT made its debut in space on the International Space Station in 2023, demonstrating NASA's first LEO user for their lasercom relay system.

Over the moon for O2O

Leading up to the launch of Artemis II, operations teams from the laboratory traveled to NASA's White Sands Test Facility and Mission Control Center (MCC) to conduct monthly maintenance on ground hardware and simulate different mission stages. During the 10-day mission, laboratory teams provided 24/7 coverage.

At mission control, one laboratory team, along with NASA Goddard colleagues, interfaced with a mission flight controller to command the O2O payload, coordinated with U.S. and Australian ground terminals to bring up the O2O physical link, assessed whether overall O2O mission requirements were being met, and analyzed data to ensure payload health and optimize performance. Another laboratory team oversaw subsystems of the optical ground terminal at White Sands, while staff at the laboratory's main campus in Massachusetts offered subject-matter expertise.

Initially, O2O had a scheduled operational window of one hour per day, with the onboard radio system set to downlink most data. However, mission operators found O2O so useful that they maximized its operational time as the mission progressed. On the fly, mission operators adjusted Orion's attitude — how the spacecraft is oriented in space — so that O2O could have line-of-sight access with the ground.

"One special aspect of this mission that enabled our technology to be so impactful was the flexibility built into the planning process to account for the fact that humans hadn't been to the moon in more than 50 years, and it would be the first time sending astronauts on Orion," says Bryan Robinson, leader of the Optical and Quantum Communications Group. "An established process for making real-time changes to the plan and the willingness of operators to try out this new technology had a huge impact, even for this short mission. This impact was tangible by everyone in mission operations and by the public watching from home."

With Artemis II completed, engineers, scientists, and mission specialists are analyzing mission data. Their analyses will provide insights into spacecraft and subsystem performance and moon geology, which will inform lunar landings and deep-space exploration. While the laboratory team is still processing O2O performance data, they believe the system could downlink at least 10 times more data by improving the efficiency of the downlink process and by addressing data-flow bottlenecks in space and ground networks.

The laboratory team is now evaluating how lasercom could support future moon plans for Artemis and Ignition. Aligning with the National Space Policy to secure U.S. leadership in space, Ignition is a recently announced initiative to establish a permanent lunar base with a sustainable human presence.

"Participating in this historic mission from the MCC and having O2O be useful, I couldn't have asked for anything more amazing in my career," Khatri says.

"When I came home, I was floored by the response of people who engaged with the mission while it was happening. Much of that engagement was enabled by the technology we developed. That's a rare moment in a career doing what we do," Robinson adds.

Reprinted with permission of MIT News.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World