Your voice, your typing, your sleep – what workplace wellbeing apps are really analysing

Mohammad Hossein Amirhosseini, University of East London

Image: Cottonbro studio / Pexels

A workplace wellbeing app might seem like a simple and helpful tool – a mood check-in, some stress management advice, or a chatbot asking how your week has gone. But behind that supportive language, some systems are also quietly analysing your voice, writing style and digital behaviour for signs of psychological distress.

These tools are already on the market – aimed at workplaces, universities and healthcare. They are framed as early-intervention systems that promise to cut costs and identify problems before they become serious. Unfortunately, companies are under no obligation to report using them, so data about how widespread they are is lacking.

The basic idea behind these tools is that behaviour leaves patterns. Artificial intelligence (AI) systems trained on large datasets learn to recognise signals associated with particular mental health conditions, and when similar signals appear in new data, the system produces a probability estimate.

For many people, the surprising part is how much ordinary behaviour can reveal. Voice recordings can pick up changes in rhythm, pitch and hesitation. Language models can analyse word choice and emotional tone. Smartphone data has also been explored as a way of tracking changes in sleep, movement and social interaction – all without the person doing anything out of the ordinary.

But detecting a statistical signal is very different from identifying a genuine problem. Human behaviour is deeply contextual. Someone may speak slowly because they are tired, nervous or communicating in a second language. Reduced online activity might simply reflect a busy week.

Even well-designed systems will make mistakes. A person who is genuinely struggling may not show the behavioural patterns the system was trained to recognise, while someone else may be incorrectly flagged as being in distress.

The pressure to develop these tools is real. The World Health Organization estimates that depression and anxiety cost the global economy US$1 trillion (£800 million) a year in lost productivity. Universities report rising demand for counselling, and employers are dealing with burnout and stress-related absence. Automated early-warning systems can seem like an attractive answer.

When wellbeing becomes surveillance

But this technology can change something fundamental about how mental health is understood. Traditionally, mental health is assessed through conversations between a person and a therapist, where context matters enormously. These systems work differently, inferring psychological states from behavioural traces that were never intended to communicate emotional information.

Once those inferences are made, they can influence decisions well beyond healthcare. Assessments of someone’s emotional state could shape workplace programmes, student support systems or insurance models, affecting how institutions judge a person’s reliability or suitability for a role. In effect, psychological states become a new kind of data.

There are particular risks for some groups. Neurodivergent people often communicate in ways that differ from the norms assumed by many datasets. Someone speaking in a second language may pause more frequently, producing speech patterns an algorithm could misinterpret. A person going through grief or illness may display signals that resemble those associated with mental health conditions – without actually having one.

Used carefully by healthcare professionals, these tools could have genuine value – helping therapists spot early warning signs of deteriorating mental health. But the same capability looks very different when deployed across a workplace or university without people’s knowledge.

At a minimum, people should know when these tools are being used, what data is being analysed and whether the system has been independently tested. A claim that software can detect distress is not, on its own, enough.

Mohammad Hossein Amirhosseini, Associate Professor, Computer Science and Digital Technologies, University of East London

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

Reviewed by Asim BN.

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by External Contributor via Digital Information World

‘Manners for machines’: how new rules could stop AI scrapers destroying the internet

T.J. Thomson, RMIT University; Daniel Angus, Queensland University of Technology; Jake Goldenfein, The University of Melbourne, and Kylie Pappalardo, Queensland University of Technology

Australians are among the most anxious in the world about artificial intelligence (AI).

This anxiety is driven by fears AI is used to spread misinformation and scam people, anxiety over job losses, and the fact AI companies are training their models on others’ expertise and creative works without compensation.

AI companies have used pirated books and articles, and routinely send bots across the web to systematically scrape content for their models to learn from. That content may come from social media platforms such as Reddit, university repositories of academic work, and authoritative publications like news outlets.

In the past, online scraping was subject to a kind of detente. Although scraping may sometimes have been technically illegal, it was needed to make the internet work. For instance, without scraping there would be no Google. Website owners were OK with scraping because it made their content more available, according with the vision of the “open web”.

Under these conditions, scraping was managed through principles such as respect, recognition, and reciprocity. In the context of AI, those are now faltering.

A new online landscape

Many news outlets are now blocking web scrapers. Creators are choosing not to use certain platforms or are posting less.

Barriers are being put in place across the open web. When only some can afford to pay to access news and information, then democracy, scientific innovation and creative communities are all harmed.

Exceptions to copyright infringement, such as fair dealing for research or study, were legislated long before generative AI became publicly available. These exceptions are no longer fit for purpose in an AI age.

The Australian government has ruled out a new copyright exception for text and data mining. This signals a commitment to supporting Australia’s creative industries, but leaves great uncertainty about how creative content can be managed legally and at scale now that AI companies are crawling the web.

In response, the international nonprofit Creative Commons has proposed a new voluntary framework: CC Signals.

Creative Commons licences allow creators to share content and specify how it can be used. All licences require credit to acknowledge the source, but various additional restrictions can be applied. Creators can ask others not to modify their work, or not to use it for commercial purposes. For example, The Conversation’s articles are available for reuse under a CC BY-ND licence, which means they must be credited to the source and must not be remixed, transformed, or built upon.

Summary of CC licences. Creative Commons

How would CC Signals work?

The proposed CC Signals framework lets creators decide if or how they want their material to be used by machines. It aims to strike a balance between responsible AI use and not stifling innovation, and is based on the principles of consent, compensation, and credit.

Simplistically, CC Signals work by allowing a “declaring party” – such as a news website – to attach machine-readable instructions to a body of content. These instructions specify what combinations of machine uses are permitted, and under what conditions.

CC Signals are standardised, and both humans and machines can understand them.

This proposal arrives at a moment that closely mirrors the early days of the web, when norms around automated access (crawling and scraping) were still being worked out in practice rather than law.

A useful historical parallel is robots.txt, a simple file web hosts use to signal which parts of a site can be accessed by the bots that crawl the web and look for content. It was never enforceable, but it became widely adopted because it provided a clear, standardised way to communicate expectations between content hosts and developers.

CC Signals could operate in much the same spirit. But, as with any system, it has potential benefits as well as drawbacks.

The pros

The framework provides more nuance and flexibility than the current scrape/don’t scrape environment we’re in. It offers creators more control over the use of their content.

It also has the potential to affect how much high-quality content is available for scraping. Without access to high-quality data, AI’s biases are exacerbated and make the technology less useful.

The framework might also benefit smaller players who don’t have the bargaining power to negotiate with big tech companies but who, nonetheless, desire remuneration, credit, or visibility for their work.

The cons

The greatest challenge with CC Signals is likely to be a practical one – how to calculate, and then enforce, the monetary or in-kind support required by some of the signals.

This is also a major sticking point with content industry proposals for collective licensing schemes for AI. Calculating and distributing licence fees for the thousands, if not millions, of internet works that are accessed by generative AI systems around the world is a logistical nightmare.

Creative Commons has said it plans to produce best-practice guides for how to make contributions and give credit under the CC Signals. But this work is still in progress.

Where to from here?

Creative Commons asserts that the CC Signals framework is not so much a legal tool as an attempt to define “manners for machines”. Manners is a good way to look at this.

The legal and practical hurdles to implementing effective copyright management for AI systems are huge. But we should be open to new ideas and frameworks that foreground respect and recognition for creators without shutting down important technological developments.

CC Signals is an imperfect framework, but it is a start. Hopefully there are more to come.

T.J. Thomson, Associate Professor of Visual Communication & Digital Media, RMIT University; Daniel Angus, Professor of Digital Communication, Director of QUT Digital Media Research Centre, Queensland University of Technology; Jake Goldenfein, Associate Professor, Melbourne Law School, The University of Melbourne, and Kylie Pappalardo, Associate Professor, School of Law, Queensland University of Technology

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

Reviewed by Asim BN.

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• Online ad fraud is a feature, not a bug

by External Contributor via Digital Information World

Online ad fraud is a feature, not a bug

By Benjamin Kessler

Image: Erik Mclean / Unsplash

Technological advancements and the dynamics of the platform economy make rooting out fraud more complicated than it may seem.

With print media circulation and broadcast television viewership in free fall, a lot is riding on the online advertising space being able to take up the slack. The good news is, digital ad spend is booming.

The bad news? A good chunk of that money is chasing a mirage.

Online ad fraud—where ad publishers falsely inflate engagement metrics (impressions, clicks, etc.) to boost revenues—is a growing problem that eats upwards of 20 percent of global ad spend.

Min Chen and Abhishek Ray, both professors in the information systems and operations management area at Costello College of Business at George Mason University, are researching how online ad networks, such as Google Ads, can improve upon existing anti-fraud methods. Their recently published paper in Management Science explores deep-rooted dynamics of the online ad ecosystem that make eliminating fraud even more complicated than it may seem at first glance. The paper was co-authored by Subodha Kumar of Temple University.

The researchers used a game-theoretic model to replicate the interconnected decision-making of the three players involved: advertisers, publishers, and the networks that serve as go-between.

“The way the ecosystem works is that the platforms in the middle, the ad networks, shares the benefit from the transaction,” Chen explains. “People have been arguing whether the network is incentivized to put their best efforts behind deterring fraud, since the fraudulent traffic benefits the networks too. So we tried to create a model to capture this.”

“If the advertisers rely solely on the reports from the ad networks, they may be at risk. They should use third-party tools to audit the performance better.” — Min Chen, information systems and operations management professor at the Costello College of Business at George Mason University

In addition, the model incorporates the two main fraud deterrents that networks routinely use. One is technological—platforms can adopt tougher standards for fraud detection, widening the scope of suspicious activity that gets flagged. The other is economic—lowering payments to all publishers so as to disincentivize large-scale fraud.

Surprisingly, the researchers find that the online ad economy works best when the two approaches seem to be working at cross-purposes. A tightening in fraud detection technology, paired with high payments for publishers, may sometimes produce the best outcomes for advertisers, publishers, and networks, as the market evolves.

The reason is rooted in the imperfect nature of fraud detection. To be sure, detection systems are improving all the time, especially with the advent of AI. But fraudsters do their best to blend in and adapt, using technological tools that often outpace those of their pursuers. “You cannot catch all the fraud, and if you try, you are going to mis-detect a lot of non-fraud,” Chen says.

Tougher fraud detection, then, will always mean more false positives, no matter how good the technology gets. To counter this inherent unfairness that penalizes good and bad actors alike, the ad network’s payment to publishers need to go up. Otherwise, publishers may take their business elsewhere—especially those most valuable to the system, i.e. those that are trustworthy — thereby decreasing the advertisers’ valuation on ad traffic.

“These ad networks are kind of a unique system where you can be monetarily rewarded for being honest, or punished for being dishonest,” Ray says. “What we discover for this system is there can be a way in which we can give carrots to people, not just sticks.”

On a similar note, the researchers find that an attempt to purge “bad apple” advertisers from the system can backfire due to false positives. In fact, fraud can sharply increase if networks, believing they have solved the problem, relax their fraud detection standards and raise incentives for the remaining advertisers. “Since the publishers who produce the fraudulent traffic are fewer now, the ad network may no longer need to maintain a strict detection policy. This can encourage the remaining ones to commit much more fraud,” Chen explains.

To Ray and Chen, online ad fraud is, in at least one sense, no different from older forms of malfeasance that are found in all free societies. “We need to have some kind of mechanism for managing the level of fraud, because the fraud detection method is never going to be perfect, whether it’s financial fraud, accounting fraud, etc.,” Chen says.

But as an example of the contemporary platform economy, the online advertising ecosystem is also distinctive, in that its de facto regulatory authority has skin in the game. The ad networks’ mixed incentives—as both beneficiaries and inhibitors of fraud—can undermine integrity and trust within an already-compromised system.

“If the advertisers rely solely on the reports from the ad networks, they may be at risk,” Chen says. “They should use third-party tools to audit the performance better.”

Editor’s Note: This post was originally published on George Mason University News and republished on DIW with permission.

Reviewed by Asim BN.

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• Researchers Pioneer New Technique to Stop LLMs from Giving Users Unsafe Responses

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Researchers Pioneer New Technique to Stop LLMs from Giving Users Unsafe Responses

By Matt Shipman, NC State News

Image: Nahrizul Kadri / Unsplash

Researchers have identified key components in large language models (LLMs) that play a critical role in ensuring these AI systems provide safe responses to user queries. The researchers used these insights to develop and demonstrate AI training techniques that improve LLM safety while minimizing the “alignment tax,” meaning the AI becomes safer without significantly affecting performance.

LLMs, such as ChatGPT, are being used for an increasing number of applications – including people asking for advice or instructions on how to perform a variety of tasks. The nature of some of these applications means that it is important for LLMs to generate safe responses to user queries.

“We don’t want LLMs to tell people to harm themselves or to give them information they can use to harm other people,” says Jung-Eun Kim, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.

At issue is a model’s safety alignment, or training protocols designed to ensure that the AI’s outputs are consistent with human values.

“There are two challenges here,” says Kim. “The first challenge is the so-called alignment tax, which refers to the fact that incorporating safety alignment has an adverse effect on the accuracy of a model’s outputs.”

“The second challenge is that existing LLMs generally incorporate safety alignment at a superficial level, which makes it possible for users to circumvent safety features,” says Jianwei Li, first author of the paper and a Ph.D. student at NCState. “For example, if a user asks for instructions to steal money, a model will likely refuse. But if a user asks for instructions to steal money in order to help people, the model would be more likely to provide that information.

“This second challenge can be exacerbated when users ‘fine-tune’ an LLM – modifying it to operate in a specific domain,” says Li. “For example, an LLM may have good safety performance. But if a user wants to modify that LLM for use in the context of a specific business or organization, the user may train that LLM on additional data. Previous research shows us that fine-tuning can weaken safety performance.

“Our goal with this work was to provide a better understanding of existing safety alignment issues and outline a new direction for how to implement a non-superficial safety alignment for LLMs.”

To that end, the researchers created the Superficial Safety Alignment Hypothesis (SSAH), which neatly captures how safety alignment currently works in LLMs. Basically, it holds that superficial safety alignment views a user request as binary, either safe or unsafe. In addition, the SSAH notes that LLMs currently make the binary determination on whether to answer the request at the beginning of the answer-generating process. If the request is deemed safe, a response is generated and provided to the user. If the request is deemed not safe, the model declines to generate a response.

The researchers also identified safety-critical “neurons” in LLM neural networks that are critical for determining whether the model should fulfill or refuse a user request.

“We found that ‘freezing’ these specific neurons during the fine-tuning process allows the model to retain the safety characteristics of the original model while adapting to new tasks in a specific domain,” says Li.

“And we demonstrated that we can minimize the alignment tax while preserving safety alignment during the fine-tuning process,” says Kim.

“The big picture here is that we have developed a hypothesis that serves as a conceptual framework for understanding the challenges associated with safety alignment in LLMs, used that framework to identify a technique that helps us address one of those challenges, and then demonstrated that the technique works,” says Kim.

“Moving forward, our work here highlights the need to develop techniques that will allow models to continuously re-evaluate and re-select their reasoning direction – safe or unsafe –throughout the response generation process,” says Li.

The paper, “Superficial Safety Alignment Hypothesis,” will be presented at the Fourteenth International Conference on Learning Representations (ICLR2026), being held April 23-27 in Rio de Janeiro, Brazil.

The researchers have made relevant code and additional information available at: https://ssa-h.github.io/.

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

Reviewed by Ayaz Khan.

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Why you may be paying more than you need to for digital subscriptions

Erhan Kilincarslan, University of Huddersfield

Image: Vitaly Gariev / Unsplash

The way we watch TV, listen to music, order groceries and take photos has changed in the past decade or so. For many of us, all of these activities involve a monthly payment.

Subscriptions have quietly become a major part of household spending across the world. But many people underestimate how much they actually pay. And there is evidence which suggests that the design of subscription services – combined with common human traits – can make these payments easy to overlook.

In the UK, consumers spend around £26 billion a year subscribing to everything from digital media to cosmetics and coffee. (Around 69% of UK households subscribe to at least one video streaming service such as Netflix or Amazon Prime Video.)

And a few small monthly payments can quickly add up. Data from Barclays bank suggests that individual consumers spend £50.60 on – so more than £600 a year. It also shows that spending on digital content and subscription services has increased by nearly 50% since 2020. In households where several people hold subscriptions, the combined spending can be considerably higher.

The result is a subscription economy that is growing faster than many consumers realise. And one reason households underestimate their spending is that some subscriptions continue running even when people no longer use them.

The UK government estimates that of the 155 million subscriptions currently active in the UK, nearly 10 million are unwanted – at a cost to consumers of £1.6 billion each year.

The charity Citizens Advice has calculated that over £300 million a year is spent on subscriptions that people are not actually using, often because they automatically renewed after a free trial.

In many cases the individual payments are small, which makes them easy to miss in a bank statement.

Behavioural economics offers one explanation. Research shows that people tend to evaluate spending using what’s known as “mental accounting” – the tendency to treat small payments separately instead of thinking about how they add up overall. As a result, people group purchases into categories rather than looking at the total amount leaving their bank account.

A £9.99 streaming subscription or a £4.99 app service may not feel significant on its own. But when several subscriptions accumulate, the combined cost can become substantial.

Another factor is automatic renewal. Many services continue charging unless customers actively cancel. This interacts with what behavioural scientists call “status quo bias”, the tendency to stick with the default option.

When cancelling requires effort or attention, people often postpone the decision and continue paying.

Consumer groups have also raised concerns about so called subscription traps. These occur when people are unintentionally signed up to recurring payments or find it difficult to cancel them.

It has been claimed that more than 20 million adults in the UK have signed up to a subscription without realising it and about 4.7 million people are still paying for one they did not knowingly sign up to.

These cases often involve free trials that automatically convert into paid subscriptions or online sign up processes where the recurring payment is not clearly explained.

Researchers studying digital interfaces have also identified design practices that make subscriptions easier to start than to cancel, sometimes described as “dark patterns” in online design.

New rules

The growing scale of the problem has attracted regulatory attention. The UK government has introduced measures aimed at tackling subscription traps, including clearer information about recurring payments and easier cancellation processes. A consultation is now taking place on how these rules will be implemented before they come fully into force.

The goal is to ensure that consumers understand the financial commitment they are entering when signing up to a subscription service.

The new measures will probably help reduce some accidental subscriptions, particularly those created through unclear sign-up processes or free trials that automatically convert into paid plans. And it seems sensible to make sure that subscription contracts contain clearer information and easier cancellation rights to help consumers avoid unwanted recurring payments.

But behavioural factors such as inertia and automatic renewal mean the problem may not disappear entirely. Even when cancellation is straightforward, consumers often delay reviewing small recurring payments, allowing subscriptions to continue.

For households, digital spending often feels invisible. Subscriptions are typically spread across multiple platforms and paid automatically through bank cards or direct debits. Without a deliberate review of monthly statements, it can be difficult to see how much these payments add up to.

Subscriptions can offer convenience and flexibility. But as the subscription economy continues to grow, it can also quietly increase household spending in ways that many consumers barely notice.

Erhan Kilincarslan, Reader in Accounting and Finance, University of Huddersfield

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

Reviewed by Asim BN.

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• Instagram, Facebook, TikTok Engagement Rose in Q1 2026 While Snapchat Declined

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Instagram, Facebook, TikTok Engagement Rose in Q1 2026 While Snapchat Declined

By Adam Blacker, Apptopia

Every quarter, we look at average time spent per daily active user across major US social platforms using Apptopia’s consumer device panel. The Q1 2026 data stands out for one reason: three of four platforms grew engagement year-over-year. Snapchat didn’t.

Comparing Q1 2025 to Q1 2026, Instagram [NASDAQ: META] grew Average Time Spent per DAU by 9.8%. Facebook grew 10.3%. TikTok grew 6.7%. Snapchat [NYSE: SNAP] declined 2.5%.

That alone would be notable. What makes it more significant is where Snap was twelve months ago. Q1 2025 was the strongest first quarter in Snap’s recent history on this metric. Time spent surged 35.8% versus Q1 2024. The 17-25 cohort, Snap’s core franchise demographic, spiked 43.6%. The product was gaining traction across every age group.

Q1 2026 reversed nearly all of it. The 17-25 cohort went from +43.6% to -0.5%. The 26-35 group went from +21.6% to -0.4%. Full-year 2025 data confirms Snap carried momentum through the middle of the year; annual growth was 16.0% for all users, meaning the reversal is recent.

The wider pattern is just as telling. Over the three Q1 periods in our study, Snap’s time spent growth rates were 8.2%, then 35.8%, then -2.5%. That’s a 38pp spread between the highest and lowest readings. Facebook’s equivalent spread was 4 points (6.3%, 8.1%, 10.3%). TikTok’s was 5 points. Instagram’s was 10.5 points, decelerating gradually from a high base. Snap is the outlier on consistency by a wide margin. Its average Q1 growth of 13.8% looks similar to Instagram’s 14.0%, but the path is a spike and a crash versus a steady glide. For anyone building a forward estimate around Snap’s engagement trends, that volatility is the problem. You can underwrite a growth rate that compounds quarter after quarter. You can’t underwrite one that swings 38 points.

“When one platform reverses while the rest of the sector keeps growing, it’s not something macro going on,” said Tom Grant, VP of Research at Apptopia. “If Gen Z were broadly pulling back from social apps, you’d see it everywhere. You do not. So not only is SNAP seeing a Q1 decline while others rise, it is now experiencing rising volatility as a business.”

The rest of the competitive set held up. Facebook posted its third consecutive Q1 acceleration, with growth concentrated in the 26-45 age range — the highest-CPM demographic in digital advertising. Instagram grew across every cohort, led by 26-35 at 14.1%. TikTok still commands the most absolute time per user in every age group, roughly 2x Facebook and 2x Instagram, and its time spent growth of 6.7% was positive if unspectacular.

Time spent across major social platforms continues to grow (time spent on mobile as well), but the consistency of that growth increasingly separates the pack. For investors, the Q1 data suggests the more durable engagement stories right now sit with Meta and TikTok, while Snap’s trajectory remains the one that needs the most proving out.

Note: This post was originally published on Apptopia blog and is republished on DIW with permission.

Reviewed by Irfan Ahmad.

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How AI English and human English differ – and how to decide when to use artificial language

Laura Aull, University of Michigan

Suspicion and affection. Apprehension and excitement. Most people have mixed feelings about AI English, whether or not they always recognize it. When reading text generated by AI, people feel it sounds off, or fake. When reading English by a human, people are more likely to feel it has a characteristic voice or a personal touch.

Image: Airam Dato-on - Pexels

What exactly makes English sound human, or sound like AI? And does it matter if AI English never truly achieves a human feel?

I research the institutionalization of English. There is a long, problematic history of people feeling positively or negatively toward different kinds of English, rewarding how it is spoken or written by some sectors of society and devaluing how it is used by others.

When generative AI language tools came along, they scaled up these problems. English-based large language models are trained on text from the public internet. Human instructions tell the models to sound like formal English. Because of that, large language models end up trained on all the bias baked into standardized human texts and ideas.

In my work, I encounter people who would never trust the internet to tell them what is right and wrong, yet they trust generative AI to tell them how to write.

Human vs. AI

The first step to becoming a more informed user of AI English is to try to understand what people mean when they say writing sounds human. This understanding will improve your AI literacy. Most importantly, it will allow you to learn to recognize two qualities that make human English different from AI English: variation and readability.

Human English contains persistent, if subtle, linguistic patterns of variation and readability. By contrast, AI uses what I call exam English – a rather formal, dense English that is favored in academic tests and papers. It is less varied and less readable. People perceive it as robotic, but they also perceive it as smart.

Here’s a quick test: Read the two text messages below and guess which one is by a human and which one is by ChatGPT.

“i’m not sure how to break this to you. there’s no easy way to put it…i can’t make the friday-night fun. sorry. however, feel free to text me during the evening if there are any lulls in conversation. anyway, hope ur exotic trip goes well. see u next term.”

“Hey! I’m really sorry, but I won’t be able to make it Friday night. I hope you all have a great time, and I’ll see you next term!”

A human reader would probably notice several patterns right away. The first message has more “textese”: It defaults to lowercase and includes phonetic spellings “ur” and “u.” The second text has exam English capital letters, commas and spelling.

People are likely to have other impressions, too. Perhaps the first text feels more personal, and less sure of itself. Maybe the second text feels stiff, like it was written by an acquaintance. The first text contains different kinds of phrases and clauses, while the second text repeats the same clause structure four times.

On some level, human readers pick up on such patterns. Most people would say that the first text is by a human and the second is by AI. Indeed, the second passage was generated by ChatGPT.

Even this basic illustration shows that human English includes variation in word usage and grammatical structures that breaks up information and conveys personal meaning. AI English has less variation and more dense noun phrases. In research studies, these patterns appear repeatedly across genres and registers.

Some AI English patterns change

AI writing tools evolve, and large language models vary. GPT 5 was infamously cold-sounding compared with its predecessor GPT 4, for example.

But the patterns I am talking about are likely to persist. AI English favors what exam English has always rewarded: homogeneity and information density. And thus far, instructional tuning – training AI models to follow human instruction – only makes AI English less like human English. It doesn’t help that AI writing is part of what AI bots train on.

The net effect today is that AI English has been trained on English that is much more narrow than actual, collective human English in practice. Humans, by contrast, don’t just use language that is probable, but language that is possible – based on the varied language use they have observed, their creative capacity for new utterances and their propensity to blend personal and impersonal language patterns.

AI models can produce conventionally correct, smart-sounding language, but that language lacks the variation, accessibility and creativity that make language human.

How AI and human English can coexist

If you can become more aware of differences between AI and human English, those insights can help you use both language forms more productively. Here are a few steps to take:

Use language labels. When describing a given passage, use labels like “dense,” “plain,” “interpersonal” or “informational”, not social labels like “sounds smart” or “sounds off.” Consider exploring the actual patterns in human and AI English and trying to describe language patterns, not feelings about them, in other words.

Use AI tools selectively. Not only does human English have more accessible and varied patterns, it also engages the brain more than using AI language tools. To help prevent AI English from overshadowing human varied language in the world, use AI selectively.

Use curated tools. Tools like small language models and programs that you can add to a web browser to root out bias, such as Bias Shield, can help people make principled choices about AI English use. Tools such as translingual chatbots can also bring to AI English much more of the global variation in human English.

Be conscious of what sounds smart, and why. A century and a half of exam English makes it easy to think that dense, impersonal writing patterns are smart. But like any language patterns, they have pros and cons. They are not particularly personable or readable, especially for diverse audiences, and they are not representative of the range of global English in use today.

There can be good reasons to use exam English, but not just because AI bots generate it, or because people have learned to perceive it as smarter.

At its best, AI English is a language database driven by statistics. It’s big, but it’s canned. History tells us that a full range of global human English gives people the greatest possibilities for expression and connection.

Laura Aull, Professor of English and Linguistics, University of Michigan

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

Disclosure statement: Laura Aull does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Partners: University of Michigan University of Michigan provides funding as a founding partner of The Conversation US.

Reviewed by Ayaz Khan.

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