Study Links Workplace Doomscrolling to Rumination and Lower Work Engagement

By Lesley Henton, Texas A&M University

The effects of consuming negative news during the workday can linger in employees' minds long after they stop scrolling.

Image: Vitaly Gariev - Unsplash

Whether on lunch break or on the clock, many employees scroll through the news and social media during the workday. Researchers found that employees who “doomscroll” at work suffer hidden costs that extend beyond lost productivity.

Dr. Ian Hughes, assistant professor in the Department of Psychological and Brain Sciences at Texas A&M University, led the study published in Computers in Human Behavior, finding that employees who obsessively consume negative news during the workday are more likely to get stuck thinking about what they just read. As those thoughts linger, engagement with work tends to suffer.

“Doomscrolling, or the act of obsessively scrolling through social media with a focus on negative or otherwise distressing information, is something that is growing more and more common among all age groups, but particularly folks between the ages of 18 and 35 across the world,” said Hughes, whose research focuses on the intersection of organizational behavior and occupational health psychology.

Across two studies involving workers in the United States and United Kingdom, Hughes and his colleagues found a consistent pattern: employees who doomscroll at work are more likely to ruminate on the negative information they encounter, and that rumination contributes to lower work engagement. The relationship was especially strong among people with higher levels of neuroticism, a personality trait associated with anxiety and worry.

While doomscrolling is often dismissed as a productivity issue, the researchers found the consequences extend beyond the time spent looking at a screen.

“What we find is that workers who doomscroll on the clock are less engaged, in part because their mind is preoccupied, sort of replaying the images and messages that they encountered during their doomscrolling sessions,” Hughes said.

Doomscrolling is a habit that’s hard to break

Hughes said doomscrolling can be a surprisingly difficult habit to shake as people often turn to social media not because they enjoy feeling distressed, but because they are trying to make sense of uncertainty.

“It’s something that people do oftentimes as a way of soothing their anxiety,” he said. “During very uncertain, rapidly unfolding social situations, whether it’s a pandemic or a war or some sort of armed conflict, a public safety threat, something like that, we see people turn to social media and really refresh their feeds constantly to stay informed and up to date.”

In that sense, doomscrolling is not simply mindless scrolling, it’s an attempt to stay informed during moments that feel consequential or threatening. The problem, Hughes said, is that this information-seeking behavior repeatedly exposes people to distressing content that can be difficult to stop thinking about.

“Doomscrolling is a sort of double-edged sword in that it is, at its core, an information-seeking behavior,” he said. “But it’s an information-seeking behavior that exposes people repeatedly to distressing or otherwise negative information.”

Modern workplaces may be especially vulnerable because employees carry the entire news cycle with them throughout the day. Smartphones, laptops and social media feeds make it easy to check the latest developments between tasks, meetings or emails.

“For a lot of people, it’s very hard to look away from those things,” Hughes said. “The world of work represents a unique area where people don’t leave their cellphones at home.”

Setting boundaries

Despite the findings, Hughes doesn’t believe organizations should respond with strict bans on phones or social media.

“The reality is, those policies often just make people upset and they don’t really work,” he said.

Instead, he recommends setting boundaries around when and where doomscrolling happens.

“If you’re going to doomscroll, try to contain that behavior to one physical location in your life,” he said. “Rather than doing it at work, maybe it’s at home in a comfy chair, maybe at a lounge or a coffee shop or a bar, someplace where you allow yourself to doomscroll and take in some of this negative information.”

That advice is unlikely to become less relevant anytime soon. Hughes says doomscrolling is a behavior that is here to stay because access to social media continues to expand and algorithms continually feed users a stream of attention-grabbing content.

“It is important to stay informed,” he said. “What is also important, though, is that you don’t let that information-seeking occupy every moment of your life.”

More information: Working, scrolling, and worrying: Doomscrolling at work and its implications for work engagement, Computers in Human Behavior, (2023). DOI 10.1016/j.chb.2023.108130 - https://www.sciencedirect.com/science/article/pii/S0747563223004818.

Reviewed by Irfan Ahmad.

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• The free EU database that tells you if an appliance is actually green
by External Contributor via Digital Information World

The free EU database that tells you if an appliance is actually green

Lala Rukh, University of Galway; Atlantic Technological University

Image: Smart Renovations - Unsplash

When something breaks down, like a washing machine or television, you might find yourself in a showroom or flicking through 12 browser tabs, trying to choose between “eco-friendly” or “green” replacements. They look and cost the same, yet we currently have no way of knowing which claim, if any, is true.

The European Commission has found that more than half of green claims are vague, misleading or unfounded, and four in ten come with no supporting evidence at all. With over 230 sustainability and 100 green energy labels competing for our attention across the EU, no wonder Greenwashing has left consumers and researchers like me trusting none of them.

But the European Product Registry for Energy Labelling (EPREL) is a digital database that provides the hard evidence needed to cut through the noise.

Since 2019, any appliance requiring an EU energy label – washing machines, fridges, dishwashers, even tyres – must have its performance data logged in a public database before it can be sold. Over 2 million products are now registered.

Here’s how to tap into that database the next time you’re in that showroom.

Scan the QR code on the energy label or search for the specific appliance model on the EPREL website. In Great Britain, post-Brexit labels carry their own QR code with the UK flag, but most models appear in EPREL too.

What you’ll find goes beyond the label: electricity use per 100 wash cycles, water use, spin performance, noise, wet grip for tyres, the hidden energy spike of high-contrast TV settings – in other words, the numbers that decide your bills, not the adjectives.

You can also filter by sorting up to three criteria at once – energy first, water second, noise third, for example, and compare side by side. Suddenly “eco-friendly” either shows up in the data or it doesn’t. You can even check how many competitors share a rating (if half the market is A-rated, the boast means little). And if the shop label doesn’t match the register, one anonymous tap reports it straight to the manufacturer and the regulators.

Why is this not being made more obvious? A fair question. The EU-funded EPREL services survey of over 2,500 shoppers found while nine out of ten EU shoppers research products online, more than half have never noticed the QR code on an energy label, and barely one in 16 has ever scanned it.

Ironically, shoppers say they want exactly what EPREL already offers (such as complete data, filters, side-by-side comparison) but they just don’t know it’s there. The database, originally built for regulatory compliance, opened to the public in 2022 and nobody advertised it. That is changing: last month the Commission added a comparison tool and an EU-funded project is building a new app that will pair official figures with prices and availability.

Simple but strong labelling help people decide what to buy. When the EU tidied up its energy labels in 2021, scrapping the baffling A+++ business rating for a clean A to G energy scale, an experiment with more than 1,000 households found that the simpler labels nudged people towards the most efficient choice.

More than washing machines

Once you spot the pattern, you see it everywhere. Europe has been quietly stacking these tools up, one by one.

Since June 2025, smartphones and tablets also carry an energy label grading their repairability from A to E.

The system scores how crucial components (such as the battery, screen or charging port) perform on design, the tools needed for repair and the availability of long-term support. The label shows a single letter, but the database reveals the full details behind it – so a phone that’s easy to open yet short on software updates can be spotted before you buy. Tumble dryers are expected to carry the same score from 2027.

The A to E repairability grade has teeth too. Manufacturers must keep spare parts available for seven years and software updates for at least five. And the EU’s right to repair directive, in force since July 31, goes further: manufacturers of common products must repair them at a reasonable price even after the warranty ends, repair-blocking tricks (such as software that disables a replaced screen or the refusal to sell spare parts) are banned, and if you choose repair over replacement under guarantee, your guarantee extends by a full year for the whole product.

The net is widening beyond energy. The EU’s new ecodesign rulebook will attach a digital product passport to products sold in Europe – a scannable record of environmental footprint, extending EPREL’s logic to almost everything, starting with batteries from 2027.

Even slogans are on notice. From September 2026, under an EU law adopted in 2024, unproven claims like eco-friendly become illegal across the EU, leaving only independently verified or state-backed labels.

In the UK, the Competition and Markets Authority can already fine companies up to a tenth of their global turnover for misleading green claims, without going to court.

So next time an appliance dies, don’t squint at the word “eco”. Scan the QR code. Check the numbers against the promises. And if the label doesn’t match the register, report it. That tap enables you to hold a manufacturer to account.

The tool is free. It’s already in your pocket.

Lala Rukh, PhD Candidate, Energy, University of Galway; Atlantic Technological University

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

Fact-checked by Irfan Ahmad.

Editor's Note: Additional official sources were added to the third-last paragraph to support information on enforcement without court proceedings and related penalties.

Read next: Apple Leads Brand Loyalty Across Smartphones, Wearables and Headphones, but Trails in Smart Speakers
by External Contributor via Digital Information World

Apple Leads Brand Loyalty Across Smartphones, Wearables and Headphones, but Trails in Smart Speakers

By Felix Richter, Statista

Apple’s success over the past decade has not rested solely on selling individual devices, but on building an entire ecosystem of hardware and services that work together seamlessly. This has resulted in a fierce brand loyalty among Apple users, who are often fully committed to the Apple universe.

According to Statista Consumer Insights, 92 percent of U.S. iPhone users say they are likely to choose the same smartphone brand again in the future, putting Apple ahead of Samsung and Google. The same pattern appears across other product categories. Apple leads Samsung and Fitbit in wearables and smartwatches, with 89 percent of users saying they would stick with the brand. It also comes out ahead in headphones, where 89 percent of AirPods users are likely to repurchase, compared with 80 percent for Samsung and 79 percent for Beats, Apple’s subsidiary.

According to the Statista chart, smart speakers are a notable exception. Amazon leads the category, with 83 percent of its customers likely to choose an Echo speaker again, versus 76 percent for JBL and just 73 percent for Apple. One of the reasons for Apple’s relative weakness in this category is its not-so-smart assistant Siri. As the company’s HomePod speakers rely heavily on Siri, its limited functionality compared to the competition has left many users frustrated – a problem that the company hopes to address with the revamped, AI-powered Siri expected to launch this fall.

Fact-Checked by Irfan Ahmad.

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• An AI job boom? Here’s what the tedious, temporary work in data labelling is actually like

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by External Contributor via Digital Information World

An AI job boom? Here’s what the tedious, temporary work in data labelling is actually like

Fan Yang, The University of Melbourne

Image: Elise Racine, CC BY

Amid all the talk about artificial intelligence (AI) both creating and destroying jobs, a troubling reality flies under the radar.

The tasks machines can’t perform well are often offloaded onto marginalised global workers who are struggling in precarious labour markets. They do ostensibly “automated” work under exploitative conditions.

Data work is an essential part of building and refining AI systems. Before AI models can “learn” anything, human data workers must categorise, label, test and moderate vast volumes of text, images, audio and video, to make the data usable for AI training.

This labour is performed by an expanding global digital workforce that prepares the datasets not only for big tech, but also high-stakes industries such as banking, insurance, healthcare and government agencies, including defence.

To understand the AI workforce, I have been interviewing workers in China and Australia who prepare datasets for AI models. The fieldwork is ongoing, but here’s what they’ve revealed so far.

Inequality is baked in

My interviews with ten people to date show that precarious labour markets and marginalised social status have pushed digitally literate young workers into the data labelling industry.

As one interviewee said:

"We do the manual work so that they get the credit for the intelligence."

There’s a lot of inequality across the data labour market, shaped by people’s qualifications and geographic location.

Those with PhD-level or equivalent qualifications and STEM certifications can typically get more specialised tasks. If based in the Global North, such workers tend to be higher-paid, earning A$400–800 per hour depending on the task.

But such specialised and high-paid tasks are rare and difficult to get. Most workers I interviewed perform general tasks, such as repetitively drawing bounding boxes for images used in drones, self-driving cars and automated vending machines, or annotating audio.

These workers normally receive as little as A$6 per day or even less. The pay can’t cover daily expenses, and the long hours leave workers with chronic eye strain and back pain.

Part of the gig economy

Data work is not unlike other poorly regulated jobs in the gig economy.

Workers have no formal contracts and are not employees. They’re classified as “users”, and platforms simply call on them when there are tasks aligning with their expertise and track record.

User agreements exist primarily to protect the companies behind the outsourced work, such as requiring the workers don’t disclose any of the information they see.

This is despite the fact datasets are already anonymised: workers often have no way of knowing which companies they conduct data labelling for. They don’t even know if humans or AI agents assess their completed work. And they have minimal rights to appeal any assessment of their performance.

All interviewees reported getting less work over time as AI advances. What’s left are more difficult and time-consuming tasks. Interviewees expressed little concern about their jobs eventually being replaced by AI, but this apparent indifference stemmed from a pessimistic outlook:

"If I don’t make this money, someone else will, and I will be replaced [by AI] eventually anyway."

As one worker noted, what AI actually affects is the working class itself. This working class is expanding as more professionals are pushed into data labelling by the precarity of the current job market.

All work, little pay

How a worker gets paid is determined by the platform. US crowdsourcing platforms generally offer higher-paid tasks and pay workers when they submit the work.

Chinese platforms or companies often pay workers only after their tasks have been assessed and confirmed to meet preset standards. As a result, workers often spend hours completing tasks without receiving any payment.

In addition, workers in China can’t access US platforms; using a VPN to circumvent this risks triggering an account ban.

Companies prefer consistency in their workforce, as turnover is costly; workers require instruction and training before they can begin a task, and further time before they can complete tasks efficiently.

As workers typically get faster the longer they stay in the role, companies want to retain the experienced ones. But many workers leave because the pay is so poor.

To offset this, companies have turned to recruiting more vulnerable groups. One example is collaborating with local government initiatives supporting disabled people. These workers are less likely to quit because the job is often their last resort.

Workers reported they were unable to find other employment or were in the process of searching for full-time positions, due to disability, pregnancy or being recent graduates.

The bigger picture is grim

The AI economy has created jobs. But many of these involve human workers correcting errors and handling tasks too difficult or ambiguous for machines to resolve. This work is often more cognitively and emotionally demanding than what it replaced.

And human workers don’t even know if they’re answering to human managers or AI agents. This weakens their right to bargain.

Data workers are effectively the disposable batteries of the AI economy: drained of every last charge, then discarded once they can no longer power the system that depended on them.

Australia is accelerating the pursuit of an AI-driven economy. The crucial question is not how many jobs are created, but what kind of jobs they are.

The employment gains AI promises may only exist in the short term, and come at the cost of data workers’ life quality and wellbeing. We must establish protections and a long-term plan for this workforce, so we can prevent the harm rather than merely respond to it after the fact.

Fan Yang, Research Fellow at Melbourne Law School, The University of Melbourne

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

Reviewed by Irfan Ahmad.

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• More phones may soon let us prove photos are real – but will it solve the AI fake image crisis?

• Mid-Year Metric Most Businesses Forget: Their Visibility to Customers

by External Contributor via Digital Information World

Mid-Year Metric Most Businesses Forget: Their Visibility to Customers

By Heather Holmes

Image: Andrea Piacquadio - Pexels

We’re just crossing the midpoint of the year. And as most businesses are auditing the first six months of 2026, they review the usual numbers like revenue, pipeline expenses, office overheads, and headcount, there is one metric that rarely makes the half year list: visibility. I’m talking about whether a company can still be found by customers and even recommended when others are asking. And increasingly by using an AI tool rather than a search engine.

More and more customers are now starting their research using AI tools like ChatGPT and Gemini which shortlists or recommends names instead of a page of links. This has already become a regular behavior: a Capgemini study found that 58% of consumers have replaced traditional search engines with generative AI tools for product and service recommendations. If a business that isn't on that list, they are essentially invisible at the moment a customer is deciding. That gap is worth auditing, especially for businesses that are heading towards their busiest season.

Why a mid-year visibility audit matters

Just as companies audit their financials, Heather Holmes, founder and CEO of the communications agency Publicity For Good, argues that visibility and discoverability should be given the same importance. Unfortunately, most businesses will only discover the problem of their visibility after their direct competitors start showing up where they don’t appear and when customers are asking for suggestions or recommendations.

"Companies audit their books at mid-year without a second thought, but almost none audit whether they can actually be found," Holmes said. "By the time a business notices it's invisible in AI search, a competitor has usually already become the answer."

Auditing visibility is a structured check on a brand’s authority and trust. It measures its presence and how it appears across the online channels customers use for research and recommendations such as search engines, social platforms, independent news and industry sites and AI assistants. The audit helps uncover visibility gaps. Even if brands are well-known, they can often be invisible to the eyes of AI and miss opportunities to be recommended to customers.

The warning signs of a fading footprint

Heather warns of several signals of brand weakening in its online presence:

• Dwindling inquiries from customers who have found the brand online
• Category search results with your competitors listed before your own brand
• Vague answers, outdated information or sometimes no answer at all from AI tools when asked about a company, brand or product recommendation

That last one is the revealing one. Because AI models build their answers largely from independent, third-party sources rather than a company's own website. A brand can have a polished site and active social accounts and still be absent from AI recommendations entirely.

Why PR now drives AI discoverability

Holmes believes that businesses have yet to shift to a public relations mindset. Beyond the paid ads, sponsored segments and product placements, PR has been treated as just a brand-awareness exercise. But it is now the primary driver of whether a business is discoverable by AI at all.

This happens because of how AI models work. They act like a digital detective looking first for corroboration and gather evidence from credible independent sources to verify if a brand or company is legitimate or carries authority in their field. Researchers at the University of Toronto found that AI search exhibits a systematic and overwhelming bias toward earned media such as press coverage and independent mentions while treating paid advertising, brand-owned and social content with little weight when generating AI responses. Third party mentions leave a trail of brand or business credibility that give AI models reasons to trust and recommend. Without them, AI has no data, no evidence and no information to work with.

Practical steps before the busy season

Holmes recommends a framework of proven steps that require more discipline than big budgets for business owners who want to strengthen their presence and close the holes in their visibility.

1. Find your gaps. Run the audit first. Open the most widely customer-used AI tools: ChatGPT, Gemini, Claude, and Perplexity and ask three questions:
   1. What it knows about the business by name
   2. What it recommends in the business's category
   3. And who it suggests for the specific problem the business solves

The answers will be your raw, unfiltered baseline on your presence as AI can interpret your current information and where your competitors are positioned.

1. Correct and rebuild your foundation. Consistency of information is key, especially across your website and other business directories such as Google Business Profile and LinkedIn. Mismatched data erodes confidence in your business. Your company name, address and contact information must be identical in all profiles. It is the fastest and cheapest way to resolve your most glaring inaccuracies.
2. Earn third-party coverage from credible independent news organizations. AI largely draws data from independent sources and the brands that show up in AI responses have a trail of coverage. You just need to start where it is most realistic to get a win like local stations and newspapers, trade or industry publications, and niche podcasts that generate content and mentions that AI consider as evidence. And when you and your brand is ready nationwide or regional coverage, you can pitch for major media outlets.
3. Highlight your expertise. In your industry, you have grown your business with your expertise. You have disrupted the market and solved a problem with your brand. You are an expert, but the world doesn’t know it. Let them by contributing a guest article, become an expert resource for journalists or appearing in podcasts. This attaches the founder’s name and knowledge to credible sources the AI can reference. Holmes notes that a single expert placement does more for a brand’s credibility than a month of social posts.
4. Lastly, make this a continuous effort and not just a one-time push. Brand visibility constantly changes as market shifts, your brand evolves and competitors grow. Holmes recommends rerunning visibility audits on a regular basis to track your online presence, brand strength and accuracy of messaging, consistent rise against competitors.

Brand visibility is a metric often forgotten but is becoming increasingly essential in the era of AI search and recommendation. It shouldn’t be a one-time campaign but continuous intentional efforts as it will yield undeniable benefits to your brand’s discoverability and growth. As we move into the year’s most competitive half, the advantage will go to the businesses that audits the most overlooked metric that brings in the most customer eyes and the attention of AI.

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About Author:

Heather Holmes is the founder and CEO of Publicity For Good, a communications agency specializing in earned media and Answer Engine Optimization for the AI search era. The agency has worked with more than 500 brands, and Holmes is the author of "Seen by AI, Found by Customers: The Purpose-Driven Brand's Guide to Dominating the New Era of PR."

Fact-checked by Irfan Ahmad.

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• More phones may soon let us prove photos are real – but will it solve the AI fake image crisis?
by Guest Contributor via Digital Information World

More phones may soon let us prove photos are real – but will it solve the AI fake image crisis?

T.J. Thomson, RMIT University

Image: Thai Nguyen - Unsplash

“Pics or it didn’t happen” was a thing people used to say online when they wanted to verify a claim. Technology has changed this.

We now regularly see a mix of the surreal and the fantastic. World leaders saying things they didn’t. Public figures seemingly endorsing fake products. And disasters that unfold before our eyes but didn’t really happen.

Now, anyone can create photorealistic images or videos using AI for a range of purposes. This has changed the nature of visual evidence and our relationship to photos and videos, more broadly.

The glut of AI slop we see online is eroding trust in institutions, gobbling up resources, and scamming people out of their hard-earned money.

When faced with this challenge, we might ask ourselves: what images can we trust in the age of AI? And how are individuals and organisations alike responding?

The changing nature of visual evidence

We still rely on photographs and videos as evidence in many contexts. Want to prove an UberEats delivery arrived safely and on time? Take a photo. Want to prove your behaviour was appropriate? Record a video.

But as AI is being increasingly used to scam, mislead, and deceive, big tech is trying to innovate to reduce the chaos and restore our faith in the vision our cameras capture.

Google, for example, began embedding content authenticity software – a way to prove the origin of an image – in its smartphone cameras in 2025. It uses a technology standard, C2PA, that big camera brands, such as Nikon, Sony, and Canon, have started using in their standalone cameras, too.

Similar though distinct technology might be coming to iPhones later this year. Reports surfaced earlier this month that the next version of Apple’s operating system could include a “reference image” feature. Like the software used in some Google smartphones, this tech could be used to prove that a photo you’ve taken actually came from your device and isn’t AI fakery.

The software works by sharing the raw image and accompanying metadata to Apple for verification. If everything checks out, the image receives a unique ID. The ID allows others to verify when the image was made and the device used to make it.

But there are, of course, caveats.

The limits of visual evidence

First, the feature, if it is rolled out, will allegedly be turned off by default. Users will have to manually turn it on.

Second, once turned on, the feature doesn’t work retroactively with your existing photos. It also doesn’t work automatically with all the future photos you’ll take.

You have to decide before taking a photo whether it will be one you need to later prove. This makes sense. You might not need to verify photos of your latest meal or dog’s newest outfit. But you might want to document that something you ordered online arrived broken or that a fight you see break out on the street really happened.

Third, even “verified” photos can deceive or mislead. How close or far away from the subject the photographer is impacts the angle of view and the resulting interpretation. A fast camera shutter can turn an unrepresentative facial expression into a political statement. And the action appearing in front of the lens can also be staged or posed.

Beyond technological solutions

So, what to make of all of this? A “verified” label doesn’t necessarily mean an image is true and a lack of one doesn’t necessarily mean it isn’t. These authenticity signals can provide some information – about time and device of capture – but they don’t tell a complete story.

You’ll still need to use your critical thinking skills and the wider presentation context to make sense of what you’re seeing and whether you can believe it.

Relationships matter, too. We might spend less time looking deeply into a single piece of content and, instead, put more weight into the source behind the content.

If we don’t know the source or have reason to trust it, we might be best served by moving on. This is because most adults have limited time and fact-checking skills and we encounter too many claims to verify them all.

Ultimately, provenance technology can be a useful piece in the puzzle but it doesn’t provide the entire picture.

T.J. Thomson, Associate Professor of Visual Communication & Digital Media, RMIT University

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

Fact-Checked by Irfan Ahmad.

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by External Contributor via Digital Information World

Researchers Explore What It Means To Say AI 'Thinks'

By Stefanie Johndrow, Carnegie Mellon University

Carnegie Mellon research traces today's AI rhetoric to decades-old debates about language, technology and human intelligence.

Image: Rob Coates - Unsplash

Organizations can describe artificial intelligence using familiar words like "thinking," "writing" and "learning." But according to Carnegie Mellon University historian Christopher Phillips, those terms may tell us as much about how humans talk about technology as they do about the technology itself.

In a new paper published in IEEE Annals of the History of Computing, Phillips and co-author Alison Langmead of the University of Pittsburgh examine how language surrounding artificial intelligence has evolved since the 1950s. Their research argues that descriptions of AI have long relied on what they call “strategic ambiguity” — using words that carry precise technical meanings for computer scientists while suggesting something much broader to the public.

According to Phillips, the result is a growing tendency to compare computers with people instead of describing what each does best.

“We use words like ‘smart,’ ‘read,’ ‘write’ and ‘think’ very differently when we're talking about humans than when we're talking about computers,” said Phillips, professor and head of the Department of History in CMU’s Dietrich College of Humanities and Social Sciences. “The question we wanted to ask was: Why are we using the same words at all?”

"The work Chris and I are doing focuses on how human beings have allowed computers — tools of our own invention — to become an integral part of our daily life,” Langmead said. “Because they are now enmeshed in our social world, how we talk about them and imagine them matters a great deal. We would like for there to be a larger, ongoing conversation that transcends the hype cycle about precisely what computers can and cannot do."

How language shapes the AI conversation

Rather than debating whether machines can think like humans, Phillips and Langmead looked backward, examining conversations about computing during the 1950s and 1960s — before artificial intelligence became a household term and computers became widespread.

They found that today's debates are far from new.

Some early computing pioneers embraced human-centered language to describe computers, while others deliberately chose more precise, if less elegant, descriptions of what machines actually accomplished. Computer scientist Norbert Wiener, for example, described computers as “learning” when they implemented rules that resulted in more successful outcomes. To fellow researchers, the way he used the term had a well-defined technical meaning. To broader audiences, however, it could evoke the much richer human experience of learning.

For Phillips and Langmead, that distinction matters.

Strategic ambiguity allows technical language to be easily understood across audiences while sometimes making technologies appear more humanlike than they are. The practice isn't necessarily intentional, Phillips said, but it can shape how society understands AI.

“Why can't we say the computer is executing a particular set of instructions?” Phillips said. “Why do we have to call it thinking?”

The researchers stress that this does not diminish the accomplishments of modern AI. Phillips described today's large language models as amazing technological achievements capable of producing outputs that humans immediately recognize as meaningful.

Instead, Phillips argues that their accomplishments are remarkable precisely because they differ from human cognition.

“When you ask an image generator for a dog riding a pony in a New York Mets parade, and it produces exactly what you imagined, that's amazing,” Phillips said. “But let's not call that creativity. Let's call it the technical achievement that it is.”

Lessons from computing's early history

The paper also revisits modern benchmarks used to evaluate AI systems. Tests such as Massive Multitask Language Understanding, or MMLU, and the recently introduced “Humanity's Last Exam” are frequently described as measuring machine knowledge or reasoning. Phillips and Langmead argue that those benchmarks more accurately measure classification accuracy — how well systems identify correct answers on standardized evaluations — rather than demonstrating human knowledge or understanding.

When AI is described as thinking or writing, people can begin viewing machines as competitors rather than tools. That framing, Phillips argues, risks reducing complex human activities such as creativity, learning and reading to computational outputs while overlooking the relationships, lived experiences and judgment that shape those processes.

“Most of us read poetry because we're interested in the person who wrote it,” Phillips said. “We're interested in the emotion, the lived experience and the beauty that comes from having an actual human being produce something.”

One of the paper's more surprising discoveries was how interdisciplinary conversations about computing once were.

Phillips and Langmead found that during the mid-20th century, historians, literary scholars, psychologists, engineers and computer scientists all participated in discussions about what computers should do and where they belonged in society.

“It wasn't obvious who should control computing or what role computers should play,” Phillips said. “People across disciplines were asking those questions together.”

Why precision matters today

Today, Phillips believes those broader conversations are just as important. Across Dietrich College, colleagues in the humanities and social sciences bring different disciplinary perspectives to questions about AI, language and human knowledge

“This timely study reminds us that language does not simply deliver scientific or technological ideas: It changes these ideas and it transforms our understanding of them,” said Andreea Ritivoi, William S. Dietrich Professor of English and associate dean of research in Dietrich College. “The authors' compelling plea for precision is a great opportunity to underscore the need for productive collaborations across the ‘two cultures’ of science and humanities, as C.P. Snow, one of the heroes in this article, insisted decades ago. Too often the humanities and STEM are seen as islands of sorts, but the debates around AI happen in the troubled waters between them. We remain on one island at our own peril.”

Ritivoi’s perspective on language and interdisciplinary collaboration is complemented by Rob Kass’ focus on the importance of precision in the technical and statistical dimensions of AI.

“Computational algorithms, and mathematical derivations, rely on precision. Everyday language, however, makes heavy use of individual words that have multiple context-dependent meanings: they are often understood in a particular way by small groups of people within a limited setting, but others may easily attribute to those words very different meanings,” said Kass, Maurice Falk University Professor of Statistics & Computational Neuroscience in the Department of Statistics & Data Science and the School of Computer Science Machine Learning Department. “This is a big issue in statistical reasoning from data. Phillips and Langmead convincingly document both the early appearance of ambiguous terminology in AI research and the ways it continues to be used for strategic advantage, much to the detriment of society as a whole.”

Ultimately, Phillips hopes the paper encourages a more thoughtful conversation about AI — one grounded less in sweeping claims about machine intelligence and more in an honest assessment of what these systems actually do.

“We're not arguing that these technologies aren't impressive,” Phillips said. “We're asking people to be very clear about what the machines do and don't do.”

Edited by Irfan Ahmad.

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by External Contributor via Digital Information World