Should You Accept Internet Cookies? BU Researchers Say the Open Web Could Suffer Without Them

By Andrew Thurston, The Brink, Boston University

New study finds ad revenue that supports digital publishers and content creators tumbles when cookies are removed.

Image: Vyshnavi Bisani - unsplash

It’s a choice you may face multiple times a day—and, at this point, your reaction is probably reflexive. Are you going to accept those internet cookies, reject them, or spend a little time customizing your settings?

Increasingly, internet users are pushing back against cookies—the digital crumbs used by websites and advertisers to spot returning customers—by choosing privacy-enhancing browsers or clicking that reject button. But ditching the cookies may have big implications for the free web. If digital companies, content creators, and advertisers aren’t making money from our surfing, the quality and usefulness of the products they offer might suffer too.

In a new study, Boston University researchers highlight the potential impact the loss of cookies has on advertisers and how alternative systems designed to balance privacy and revenue fail to recoup the costs.

They analyzed 200 million ad impressions—or views—worldwide and found that removing cookies cut website publishers’ revenue by more than a third. They also discovered that privacy-enhanced alternatives, notably a major Google project called Privacy Sandbox, only clawed back a small portion of that lost revenue. The findings were published in PNAS, the National Academy of Sciences’ flagship journal.

“Internet cookies—especially third-party cookies—have been central to how online advertising works,” says Garrett Johnson, a BU Questrom School of Business associate professor of marketing. Third-party cookies are those placed by an organization, like an advertiser, not connected to the site you’re on. “In our study, removing third-party cookies reduced publisher ad revenue by about 35 percent—and about 66 percent in the European Union—showing that cookies still play a major economic role in supporting the open web.” The European Union has tougher online privacy rules than much of the rest of the world.

According to Zhengrong Gu, a Questrom PhD candidate, because cookies help advertisers spot users around the web, they can better target and measure their ads. That makes advertisers’ spending more efficient, putting more ad money in the pockets of content creators and publishers. “If more users decline cookies, it would likely reduce the effectiveness of digital advertising and the revenue that supports much of the open web,” says Gu (Questrom’26).

The downside of cookies: no one really likes being followed. “Website cookies are online surveillance tools,” wrote Wayne State University researcher Elizabeth Stoycheff in a Conversation article, “and the commercial and government entities that use them would prefer people not read those notifications too closely.”

There have been a couple of different responses to the decline in cookie use. One is the implementation of paywalls and subscriptions to keep the cash flowing; another is requiring customers to use log-ins that work across multiple sites. Tech companies are also experimenting with privacy-enhancing technologies (PETs) that try to balance advertising needs with user privacy concerns. One of the best known PETs is Privacy Sandbox, Google’s now-defunct six-year experiment in cookie alternatives, which included innovations such as a browser tool that shared a customer’s interests rather than their detailed online history.

“In our study, Privacy Sandbox recovered only about 4 percent of the revenue lost when cookies were removed,” says Shunto J. Kobayashi, a Questrom assistant professor of marketing. That weak impact was in part due to the limited adoption of the new tools and because they changed the user experience, he says, introducing “technical frictions, especially slower ad loading times.”

In their paper, the researchers write that their findings, alongside those from other studies, “informed Google’s decision to abandon its plan to replace cookies with Privacy Sandbox. The episode underscores the difficulty of aligning privacy, performance, and competition goals in digital markets.”

To examine privacy technologies in a real-world setting, the BU team used data from ad management firm Raptive, and leveraged an experiment conducted by Google and overseen by the United Kingdom’s Competition and Markets Authority. During the study, Chrome users were randomly assigned to one of three groups: cookies-enabled, cookies-disabled, or cookies replaced by Privacy Sandbox. The study included around 60 million desktop and mobile Chrome users.

“The experiment created a rare opportunity for independent, large-scale evaluation open to external participants,” says Johnson, an expert on digital marketing who has studied privacy regulations, online ad effectiveness, and the economics of digital advertising.

He adds that many European regulators are considering even tighter online privacy rules, which could have a negative impact: “Our results provide unusually strong evidence—from a global, industry-wide field experiment—that restricting cookies carries significant economic downsides that regulators should consider.”

As for users faced with that daily accept or reject decision, Johnson recognizes that everyone will make the call that works for them—but he leans toward clicking “accept.”

“From my perspective, accepting cookies creates substantial benefits for the advertising ecosystem and the publishers I care about,” he says, “with what I perceive to be little personal risk.”

This research received financial support from the Center for Industry Self-Regulation.

This post was originally published on Boston University's The Brink and republished here with permission.

Reviewed by Irfan Ahmad.

Read next: Your phone screen doesn’t have the same color range as the human eye – and AI widens the gap between digital images and the real thing
by External Contributor via Digital Information World

Your phone screen doesn’t have the same color range as the human eye – and AI widens the gap between digital images and the real thing

Douglas Goodwin, University of California, Los Angeles; California Institute of the Arts

Image: ricardo frantz - Unsplash

A peacock feather in sunlight shifts from blue to green to bronze as you turn it. Photograph it, and this shimmer collapses into one angle, one exposure, one compromise.

A digital image is not a record of what your eye sees. The standard color space that most digital images use was built for an older display world, when cathode-ray tube monitors swept beams of electrons across phosphor-coated glass. This standard color space made color predictable across many devices, but the compromise was a narrower range of colors for screens, cameras and image files to share.

Whatever the screen offers feels complete. It is not that your eyes cannot see more; digital images give them less to work with.

I teach a class about color at the California Institute of the Arts called Plastics, Neon, and Psychedelia, which covers the many ways color is produced: by materials, by light, by screens and by the mind.

I also have a condition called deuteranomaly, which changes the way I discriminate color, though not in the way you might imagine. A deuteranomalous eye does not simply lose color distinctions – it remaps them.

Vision researchers in Cambridge showed in 2005 that deuteranomalous observers can reliably distinguish khakis and olives that look identical to people with standard color vision. I have mistaken a traffic light for an overhead streetlamp while driving at night, but my color vision is not a shrunken copy of ordinary vision: It is a different map of the same ground.

While my eyes leave some colors uncertain, they sharpen other distinctions. Screens impose another kind of limit, though more quietly: They organize color according to their own rules, then offer that version as complete. My eye and the screen are both maps that include and exclude differently. Mine trades some distinctions for others. The screen trades range for reliability. The question for any color system is not whether it is accurate but what it keeps.

From wild green to screen green

My neon pothos houseplant is so green that it seems to generate its own light. Photograph it with a smartphone and the result is fine: The leaves are green, the picture makes sense. But the green in the photograph is not the green on the plant.

Look at the photo. Look at the plant. Then look at the photo again. The photographed leaves are muted, but not evenly. Some greens flatten while others appear boosted, as if the phone were trying to compensate for what it cannot show. The leaves on the actual plant are electric. No phone I own, no printed page and no Instax print has captured that green, though the Instax comes closer.

Here is what happens. Light bounces off the pothos and strikes the phone’s sensor, which records numbers representing the color the phone sensed. Each pixel is stored as a recipe for red, green and blue light: three values that tell a screen how much of each primary color to emit. In much of the image world, those numbers are still translated into sRGB, the default color space for ordinary digital images.

Color scientists map human color perception as a horseshoe-shaped field. A standard display space cuts a triangle from that horseshoe, enclosing only part of what the eye can see. A triangle’s straight sides cannot follow the horseshoe’s curve, so some colors always fall outside the display space.

Many modern screens can show more than sRGB, but sRGB remains the default format for ordinary digital images because it works reliably across devices and platforms. The pothos green is remapped to fit, and that remapped version is the picture you get. Screen green is not wild green.

Every medium translates color in its own way. Film does too, through chemistry, exposure, dyes and paper. The Instax print is not more accurate in any absolute sense, but it conveys the pothos differently. It reflects light from a surface rather than rebuilding color as light from a screen. The green feels denser, less flat and less removed from its source. The Instax still misses the plant’s absolute color, but it misses it in a different way.

The phone’s translation matters now because people see a thing’s color on a screen before they meet it in the world.

When AI learns from limited colors

AI image generators do not simply inherit this color gap. They can amplify it. They are not trained on the plant in front of you. They are trained on other people’s photographs of plants like it: millions of images already filtered through sensors, editing software, platform compression and the color limits described above. Many of the vivid greens were clipped or shifted before the model ever encountered them.

Ask an AI image model to generate a peacock feather and you will likely receive a competent image: the canonical eyespot, the dark pupil, the cyan ring, the gold, the magenta rim. The surviving colors are the ones the image world knows how to keep. What is missing is the iridescence.

In a real feather, the barbs can flash the same blue-green-bronze as the eyespot. A photograph fixes that shimmer to a single angle. The generated image flattens it further. Its barbs are muddy brown with faint metallic highlights. The model has learned the symbols of a peacock feather, but not the event of seeing one turn in the light.

Generative models make images from the patterns they find most often in their training images. They render ordinary brightness convincingly. The rarer effects are the ones that slip away: saturated colors, metallic flashes, structural glints. The model can still make an image that looks bright, even spectacular. But its brightness is screen-native, learned from other images on screens, not from seeing the subject itself.

The loop tightens with each pass. AI-generated images are uploaded, shared, indexed and may be folded into future training sets. When models train on material produced by earlier models, their outputs narrow over time. The rare colors are the first to go.

These images do not stay inside the machine. They can fill social media feeds and image searches until they stand in for the thing itself. The simulated green becomes the green you meet first.

Watching for the gap

Day after day, screens show you only the colors inside their range, translating anything outside that range into colors they can display. If you only ever see the beetle wing as a dull image, nothing tells you a brighter one ever existed. It will just look dull. If every picture of a plant arrives with its greens muted into the same screen-safe range, that range becomes green. You will not miss the absent colors.

One way to observe the gap is to find something vividly colored: a ripe persimmon, a peacock feather, the orange-pink at the bottom of the desert sky. Look at it before you photograph it. Stay with it. Stand close. Give it a name, even a private one. Then photograph it. Hold the image next to the thing. That distance is the gap.

The wild colors have not been erased. They have been excluded from the screen’s territory. The version that travels is what gets remembered. The thing is still there. Look for it.

Douglas Goodwin, Lecturer in Design and Media Arts, University of California, Los Angeles; California Institute of the Arts

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

Reviewed by Irfan Ahmad.

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Internet Traffic Is Surging Worldwide

By Tristan Gaudiaut, Statista

Global internet traffic has surged in recent years, more than doubling between 2020 and 2025 as digital services, streaming and cloud computing continue to expand worldwide. According to data from the International Telecommunication Union (ITU), total traffic volumes have increased sharply across both fixed (landline) and mobile networks.

As our chart shows, landline traffic remains by far the dominant channel, rising from around 3,100 exabytes in 2020 to 7,300 exabytes in 2025. Mobile data usage has also grown rapidly, climbing from about 560 to 1,500 exabytes over the same period. In both cases, Asia-Pacific accounts for the largest share, at 50 to 60 percent, with traffic more than doubling across fixed networks and reaching over 900 exabytes on mobile alone.

Other regions have followed a similar upward trajectory, albeit at lower levels. The Americas and Europe remain the second- and third-largest markets, while regions such as Africa and the Arab States have recorded particularly strong relative growth, reflecting rising connectivity and smartphone adoption.

Overall, the data highlights the accelerating scale of global data consumption, with fixed networks continuing to carry the bulk of traffic even as mobile usage expands rapidly. With one exabyte equivalent to one billion gigabytes, which is roughly equivalent to the storage capacity of about 8 million 128GB smartphones, the figures underscore the massive and growing infrastructure demands of the digital economy.

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

Reviewed by Irfan Ahmad.

Read next:

• Three ways to avoid being fooled by AI slop

• Bad Bots Are Taking Over the Web
by External Contributor via Digital Information World

Three ways to avoid being fooled by AI slop

Silvia Montaña-Niño, The University of Melbourne and T.J. Thomson, RMIT University

Image: Marten Newhall/Unsplash

Global society makes billions of images and uploads hundreds of thousands of hours of video on the internet every day.

The problem is, some of this content is misleading or downright wrong. And when it’s in visual form, it can be particularly convincing.

Take the Met Gala that happened earlier this month in New York. While photographers snapped photos of Rhianna, Beyoncé and Nicole Kidman as they strutted their stuff, others saw “photos” of celebrities, such as Rosalía, Lady Gaga and Jacob Elordi, who were actually elsewhere (the images in the below Instagram carousel are AI generated).

While this type of AI slop might seem harmless and can be easily verified, other “media fakery” is becoming far more problematic and demands more robust techniques to verify.

Traditional verification techniques are falling short as AI becomes increasingly convincing and the line between authentic and synthetic blurs. This is true across all content, from still images to moving ones and audio deepfakes.

The volume of content and the speed at which it travels doesn’t help. It also doesn’t help that fact-checking can take hours or days while fakes can be created in seconds.

First, equip yourself

Guides on detecting AI-generated content suggest multiple strategies and acknowledge there are no perfect solutions. But there are helpful things you can do.

Familiarise yourself with examples of fakes and study how they were fact-checked. This helps you understand what is possible and learn how fact-checkers sort real from fake.

Look deeply. Zoom in. Pause the content or watch it frame-by-frame. Inspect the small details. Look out for inconsistencies, textures that are flat when they shouldn’t be, or patterns that are too perfect or are inexplicably off. Does the location shown match with where the scene is purported to be? Do shadows fall naturally and do lines follow the rules of perspective?

Look widely. Are you familiar with the source? What else does it publish and how long has it been around? What do other trusted sources say? How does this depiction compare to others that are available? Or if there aren’t others available, should that give you pause?

Then, apply your learnings

Let’s take an example and work through it together.

This Facebook reel, posted by an account called “Real Talk Hub”, purports to show migrants being stopped and returned by Australian police at an airport.

Before getting too granular, let’s take stock of the opening image.

The video uses scale to show what appears to be a long stream of passengers. Some are moving toward and some are moving away from a plane. It is difficult to identify specifics in the video. The superimposed text blocks almost all of the horizon line. Shallow depth of field makes aspects in the distance blurry and hard to discern.

Many of the passengers have darker skin and are visually coded as “other”. They interact with a light-skinned police officer who takes notes on a clipboard.

The vertical video is framed carefully to not reveal identifiers like the name of the airline that seems to start with the letter “P”. This makes it difficult to search the airline’s name and whether credible sources corroborate the story that’s told.

Even though the people and scenes look realistic at first glance, the video’s integrity unravels when we slow down and look closer. People in the passenger line morph and transform.

The officer is able to single-handedly remove the paper from the clipboard and it appears to inexplicably leave white strips behind. The police vests look different to images you can find in verified media photos of the Australian Federal Police.

Taken together, all these clues suggest the video is AI-generated.

The paper on the clipboard moves in an unrealistic way, and the police vest is not accurate. Real Talk Hub/Facebook

Think like a fact-checker

Many AI-generated videos can trick you and create a very compelling narrative. So, fact-checkers have developed triangulated methodologies that examine elements beyond just what you see in the video.

One way to do this is to systematically check contextual factors – the other things surrounding the content. Our team’s research has found professional fact-checkers usually pay attention to the type of social media accounts or websites distributing suspicious media.

For this AAP verification on a video about banning dogs on the beach, it was crucial to inspect the user’s activity and posting patterns.

In addition to visual anomalies, the fact-checkers also found an invisible watermark that helped them determine the content was AI-generated.

Other things to check are how long a social media account has been operating, how often the social media account posts, and whether the account is transparent about its use of AI.

These aren’t fool-proof indicators of authenticity, though. The migrant example above comes from an account that is about five years old. It also comes from a “verified” account, which might make it feel more credible. But both Facebook and X now let users pay for this verification.

Overall, when it comes to suspect images or video, don’t just look deeply. Also look widely.

AI-generated content can increasingly fool our eyes, so you also have to look beyond what’s in the video. Taking a mixed-methods approach that considers visual and contextual clues can help. By training your ability to think like a fact-checker, you can stay safer online.

Silvia Montaña-Niño, Lecturer, Centre for Advancing Journalism, The University of Melbourne and 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.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

Bad Bots Are Taking Over the Web

By Tristan Gaudiaut, Data Journalist Statista

More than half of global web traffic is now generated by bots, accounting for 53 percent in 2025. Within that, malicious bots alone make up 40 percent, nearly matching human activity at 47 percent, according to the Thales Bad Bot Report 2026. What was once a human-dominated internet has rapidly shifted toward automation. As our chart shows, the balance looked very different just a few years ago. In 2018, humans made up 62 percent of web traffic, compared with 20 percent for malicious bots and 18 percent for benign bots. Since then, malicious activity has doubled, while the share of benign bots has declined to 13 percent.

This rise in malicious bot activity reflects a growing cybersecurity challenge. Bad bots are increasingly used to steal login credentials, extract sensitive data, spread misinformation and manipulate digital advertising. Industries such as e-commerce, finance and social media remain particularly exposed, with bot-driven fraud costing businesses billions each year. Notably, roughly one in four attacks now targets APIs, allowing bots to bypass user interfaces and operate at high speed and scale. At the same time, the nature of automation itself is evolving. AI-driven bots and emerging AI agents are accelerating this shift, moving beyond simple scraping to interacting with websites, executing workflows and acting on behalf of users. In 2025 alone, AI-driven bot attacks surged by more than 12 times compared to the previous year, underscoring how quickly this threat is scaling.

However, not all bots are harmful. Benign bots, such as search engine crawlers and chatbots, remain essential for indexing the web and supporting digital services. Nevertheless, their declining share highlights how quickly malicious automation is developing. As AI continues to advance, the growing dominance and sophistication of bots are set to remain a defining feature of the internet.

This post was originally published on Statista and republished under the Creative Commons License CC BY-ND 3.0.

Reviewed by Irfan Ahmad.

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• YouTube Says It Will Add More Visible AI Disclosure Labels and Automatically Label Some Undisclosed AI Content

• Google AI Keeps Treating a Contested Religious Question as Settled Fact
by External Contributor via Digital Information World

YouTube Says It Will Add More Visible AI Disclosure Labels and Automatically Label Some Undisclosed AI Content

In a May 27 post published by the YouTube Team, the company said that it is updating how disclosure labels appear on content created or altered with artificial intelligence tools and will begin automatically applying labels in some cases where creators do not disclose significant AI use.

The Google-owned video search engine explained that disclosure labels for “photorealistic and meaningfully AI altered or generated content” will move to more prominent positions for viewers. According to YouTube, labels on long-form videos will appear below the video player and above the description, while labels on Shorts will appear as overlays on the videos themselves.

Image: YT

The company said disclosures for “unrealistic, animated, or slightly altered” content will remain in expanded video descriptions.

YouTube also said that beginning in May 2026 it will roll out “new internal signals” to help identify AI-generated content. The company said that if a creator does not specify AI use and its systems detect “significant photorealistic AI use,” YouTube will automatically apply a label.

According to the company, creators who believe content was incorrectly identified will be able to update the disclosure status in YouTube Studio. However, YouTube said disclosures will remain permanent for content created using YouTube AI tools including Veo or Dream Screen, and for content containing C2PA metadata indicating the content was fully generative AI.

YouTube said disclosure labels will not affect recommendations or monetization eligibility.

The update reflects broader efforts by online platforms to identify AI-generated media more clearly as generative tools become more common. Supporters may view the changes as a transparency measure that could also help parents more quickly identify AI-generated or heavily AI-altered videos when children are watching online content. Critics, however, are likely to focus on the accuracy of automated detection systems and how disclosure policies will be applied in practice.

Reviewed by Irfan Ahmad.

Read next: Privacy isn’t dead – it’s just that tech companies have made it inconvenient
by AI Analysis via Digital Information World

Privacy isn’t dead – it’s just that tech companies have made it inconvenient

Sandra Matz, Columbia University

Image: Ono Kosuki - pexels

“You have zero privacy … Get over it,” Scott McNealy, then CEO of Sun Microsystems, declared in 1999.

What might have sounded like a bold claim at the turn of the millennium has turned into a self-fulfilling prophecy in today’s era of big data and artificial intelligence.

Computer algorithms – step-by-step instructions – can connect the digital breadcrumbs of your existence, including Google searches, browsing histories, social media posts, credit card records and GPS locations to paint an astonishingly accurate picture of your preferences, routines and inner mental life.

These profiles often describe people better than their closest friends and family might. Yours may even tell you something you don’t know about yourself.

And as McNealy said nearly three decades ago, many people seem to have given up on the idea of ever reclaiming their privacy. When was the last time you carefully read the terms and conditions of the products you’re using?

Why do so many people do so little to protect their privacy online? I’m a computational social scientist with a background in psychology and computer science and author of “Mindmasters: The Data-Driven Science of Predicting and Changing Human Behavior.”

In talking to my students as a business professor at Columbia University and giving public talks around the world over the past decade, I have come to realize that people often substitute the question of whether they care about their privacy with two simpler and misleading ones: Is sharing my data worth it? And am I worried about my data being out there?

These questions act as mental shortcuts. They seem reasonable, but can mask your true feelings and lead you to decisions that don’t serve your long-term interests.

The ‘it’s worth it’ fallacy

When I ask people whether they care about their online privacy, they often respond by listing the benefits they get from sharing their personal data: Google Maps navigation, Netflix recommendations, Uber rides.

These are fantastic perks, no doubt. But that’s answering a different question: Is sharing my personal data worth it?

Swapping these questions seems like a reasonable approach on the surface. People often assess value by how much it would hurt to give something up. For instance, I know that drinking five cups of coffee a day might not be great for my health, but I enjoy it too much to stop. Similarly, sharing personal data brings benefits you may be unwilling to give up.

But this substitution is problematic.

First, the upside of sharing data is typically obvious and immediate: If I share my GPS location, Google maps can tell me how to get from A to B. But the downside of sharing data is often far more nebulous and abstract. My GPS location, for example, can also reveal to anyone who collects or buys the data whether I might be at risk of depression. With the carrot in plain sight, and the stick hidden away, that’s hardly a fair battle.

Second, people’s attention naturally gravitates toward the few instances where data sharing benefits them. But those instances are the exception, not the rule. Much of your data is collected and used without any direct benefit to you at all.

Finally, even if the benefits were to outweigh the risks in a particular instance, that doesn’t mean you don’t care about privacy. Ideally, wouldn’t you prefer to enjoy these services while also maintaining a high level of privacy?

The ‘I have nothing to hide’ fallacy

A second common response is I don’t care because I have nothing to hide. This idea has been carefully nurtured by Big Tech: If you’re uncomfortable sharing your data, something must be wrong with you.

But that’s not true. Privacy isn’t about covering wrongdoing. It’s about maintaining control over your personal information and deciding how it is used.

You might not be worried about your data today, but that sense of safety can be fragile. Take history: In 1933, Germany was a democracy. In 1934, it wasn’t. Personal data, such as religious affiliation, included in the census, played a major role in enabling persecution during the Holocaust. Now imagine such regimes having access to today’s digital footprints.

That scenario may feel distant, but the principle is not. The 2022 overturning of Roe v. Wade – which had guaranteed a constitutional right to abortion for five decades – made privacy suddenly relevant for millions of American women, whose search histories, app usage and location data could suddenly be used against them.

No matter how safe you feel today, you cannot predict how your data will be used tomorrow.

When you trade privacy for convienence by giving an app access to your data, it’s import to know what you’re giving away – and who is sharing it.

Asking the right questions isn’t enough

Understanding the true value of privacy, and realizing that you care about protecting it more than you might have thought, is a necessary precursor to action. But personal motivation isn’t enough.

Managing your personal data in today’s world is time-consuming. It’s too much for even a very efficient and diligent person to read and decipher the legalese of all the terms and conditions they sign off on.

For the intention-action gap to close, the burden to protect privacy needs to shift away from individuals and toward systemic solutions. That means designing policies and technologies where the safe choice is the easy one, and where maintaining privacy doesn’t automatically mean giving up on convenience and better service. Privacy-by-design standards could include more restrictive default settings. Connected computers could process information without exchanging raw data by using decentralized networks such as federated learning. New forms of collective data governance such as data trusts could also help serve that function.

Because data is permanent but leadership is not, I believe that the real solution isn’t to expect people to outmaneuver the system that exploits them but to build one that is worthy of their trust.

Sandra Matz, Professor of Business, Columbia University

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

Reviewed by Irfan Ahmad.

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