When employees feel slighted, they work less

New research from Wharton management professor Peter Cappelli reveals how even the slightest mistreatment at work can result in lost productivity.

Small slights from a manager may seem like no big deal, but new research from Wharton reveals that even the mildest of mistreatment at work can affect more than just employee morale.

The study finds that when managers at a national retail chain failed to deliver birthday greetings on time, it resulted in a 50% increase in absenteeism and a reduction of more than two working hours per month. The lost productivity was a form of revenge, with slighted employees taking more paid sick time, arriving late, leaving early, and taking longer breaks.

“Insults are about a lack of respect, and that’s what this is really all about. There are huge and small lacks of respect, but they all leave a mark,” says Wharton management professor Peter Cappelli, who conducted the study with Liat Eldor and Michal Hodor, both assistant professors at Tel Aviv University’s Coller School of Management.

The study, “The Lower Boundary of Workplace Mistreatment: Do Small Slights Matter?”, is published in the journal Proceedings of the National Academy of Sciences. While there are a growing number of papers that examine the effects of severe workplace mistreatment such as sexual and physical harassment, the study is the first to measure the cause and effect of minor infractions.

This post was originally published on Penn Today, University of Pennsylvania, on January 15, 2026, by Knowledge at Wharton, and republished on DIW with permission. Edited by Asim BN.

Image: Yan Krukau / Pexels

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by External Contributor via Digital Information World

AI-generated text is overwhelming institutions – setting off a no-win ‘arms race’ with AI detectors

Bruce Schneier, Harvard Kennedy School and Nathan Sanders, Harvard University

Image by upklyak on Freepik

In 2023, the science fiction literary magazine Clarkesworld stopped accepting new submissions because so many were generated by artificial intelligence. Near as the editors could tell, many submitters pasted the magazine’s detailed story guidelines into an AI and sent in the results. And they weren’t alone. Other fiction magazines have also reported a high number of AI-generated submissions.

This is only one example of a ubiquitous trend. A legacy system relied on the difficulty of writing and cognition to limit volume. Generative AI overwhelms the system because the humans on the receiving end can’t keep up.

This is happening everywhere. Newspapers are being inundated by AI-generated letters to the editor, as are academic journals. Lawmakers are inundated with AI-generated constituent comments. Courts around the world are flooded with AI-generated filings, particularly by people representing themselves. AI conferences are flooded with AI-generated research papers. Social media is flooded with AI posts. In music, open source software, education, investigative journalism and hiring, it’s the same story.

Like Clarkesworld’s initial response, some of these institutions shut down their submissions processes. Others have met the offensive of AI inputs with some defensive response, often involving a counteracting use of AI. Academic peer reviewers increasingly use AI to evaluate papers that may have been generated by AI. Social media platforms turn to AI moderators. Court systems use AI to triage and process litigation volumes supercharged by AI. Employers turn to AI tools to review candidate applications. Educators use AI not just to grade papers and administer exams, but as a feedback tool for students.

These are all arms races: rapid, adversarial iteration to apply a common technology to opposing purposes. Many of these arms races have clearly deleterious effects. Society suffers if the courts are clogged with frivolous, AI-manufactured cases. There is also harm if the established measures of academic performance – publications and citations – accrue to those researchers most willing to fraudulently submit AI-written letters and papers rather than to those whose ideas have the most impact. The fear is that, in the end, fraudulent behavior enabled by AI will undermine systems and institutions that society relies on.

Upsides of AI

Yet some of these AI arms races have surprising hidden upsides, and the hope is that at least some institutions will be able to change in ways that make them stronger.

Science seems likely to become stronger thanks to AI, yet it faces a problem when the AI makes mistakes. Consider the example of nonsensical, AI-generated phrasing filtering into scientific papers.

A scientist using an AI to assist in writing an academic paper can be a good thing, if used carefully and with disclosure. AI is increasingly a primary tool in scientific research: for reviewing literature, programming and for coding and analyzing data. And for many, it has become a crucial support for expression and scientific communication. Pre-AI, better-funded researchers could hire humans to help them write their academic papers. For many authors whose primary language is not English, hiring this kind of assistance has been an expensive necessity. AI provides it to everyone.

In fiction, fraudulently submitted AI-generated works cause harm, both to the human authors now subject to increased competition and to those readers who may feel defrauded after unknowingly reading the work of a machine. But some outlets may welcome AI-assisted submissions with appropriate disclosure and under particular guidelines, and leverage AI to evaluate them against criteria like originality, fit and quality.

Others may refuse AI-generated work, but this will come at a cost. It’s unlikely that any human editor or technology can sustain an ability to differentiate human from machine writing. Instead, outlets that wish to exclusively publish humans will need to limit submissions to a set of authors they trust to not use AI. If these policies are transparent, readers can pick the format they prefer and read happily from either or both types of outlets.

We also don’t see any problem if a job seeker uses AI to polish their resumes or write better cover letters: The wealthy and privileged have long had access to human assistance for those things. But it crosses the line when AIs are used to lie about identity and experience, or to cheat on job interviews.

Similarly, a democracy requires that its citizens be able to express their opinions to their representatives, or to each other through a medium like the newspaper. The rich and powerful have long been able to hire writers to turn their ideas into persuasive prose, and AIs providing that assistance to more people is a good thing, in our view. Here, AI mistakes and bias can be harmful. Citizens may be using AI for more than just a time-saving shortcut; it may be augmenting their knowledge and capabilities, generating statements about historical, legal or policy factors they can’t reasonably be expected to independently check.

Today’s commercial AI text detectors are far from foolproof.

Fraud booster

What we don’t want is for lobbyists to use AIs in astroturf campaigns, writing multiple letters and passing them off as individual opinions. This, too, is an older problem that AIs are making worse.

What differentiates the positive from the negative here is not any inherent aspect of the technology, it’s the power dynamic. The same technology that reduces the effort required for a citizen to share their lived experience with their legislator also enables corporate interests to misrepresent the public at scale. The former is a power-equalizing application of AI that enhances participatory democracy; the latter is a power-concentrating application that threatens it.

In general, we believe writing and cognitive assistance, long available to the rich and powerful, should be available to everyone. The problem comes when AIs make fraud easier. Any response needs to balance embracing that newfound democratization of access with preventing fraud.

There’s no way to turn this technology off. Highly capable AIs are widely available and can run on a laptop. Ethical guidelines and clear professional boundaries can help – for those acting in good faith. But there won’t ever be a way to totally stop academic writers, job seekers or citizens from using these tools, either as legitimate assistance or to commit fraud. This means more comments, more letters, more applications, more submissions.

The problem is that whoever is on the receiving end of this AI-fueled deluge can’t deal with the increased volume. What can help is developing assistive AI tools that benefit institutions and society, while also limiting fraud. And that may mean embracing the use of AI assistance in these adversarial systems, even though the defensive AI will never achieve supremacy.

Balancing harms with benefits

The science fiction community has been wrestling with AI since 2023. Clarkesworld eventually reopened submissions, claiming that it has an adequate way of separating human- and AI-written stories. No one knows how long, or how well, that will continue to work.

The arms race continues. There is no simple way to tell whether the potential benefits of AI will outweigh the harms, now or in the future. But as a society, we can influence the balance of harms it wreaks and opportunities it presents as we muddle our way through the changing technological landscape.

Bruce Schneier, Adjunct Lecturer in Public Policy, Harvard Kennedy School and Nathan Sanders, Affiliate, Berkman Klein Center for Internet & Society, Harvard University

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

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• ChatGPT Market Share Falls to 45% While Google Gemini Climbs to 25% in 2026

by External Contributor via Digital Information World

ChatGPT Market Share Falls to 45% While Google Gemini Climbs to 25% in 2026

By Adam Blacker - Director, PR, Apptopia Inc. Edited by Asim BN.

The GenAI Chatbot app market has increased 152% YoY since last January. Google’s Gemini has gone from a 14.7% share of the market in January 2025 to a 25.2% market share in Jan 2026. ChatGPT has fallen from 69.1% to 45.3%. ChatGPT is losing share but this makes sense as new and capable entrants have launched.

Last month, Grok’s daily active user (DAU) market share hit its highest point yet at 15.2%. This is up 3 points from its 12% share in December 2025.

The market is flattening a bit and I think that’s okay. If you’re reading this, you might be surprised to know you are most likely an early adopter of AI. While the technology itself is increasing rapidly, adoption is not as saturated as you might think.

As you can see, most people have never used a GenAI Chatbot app. If you work in tech, this could be mind blowing to realize. Still, the amount of people using multiple AI apps is growing.

About 20% of AI users are using at least two apps; a sign that people are integrating AI in their daily lives and finding out that some apps are better for certain tasks than others.

“ChatGPT built the category, but as viable alternatives have scaled, users are naturally diversifying their toolkit,” said Tom Grant, VP of Research at Apptopia. “The multi-app usage figure is the number I’d watch here. It tells me that people aren’t just experimenting anymore, they’re building workflows around specific products for specific use cases. The market could end up looking like streaming where a few major players own the market but multiple players can carve out niches based on product differentiation rather than pure network effects.”

Claude is still leading Average Time Spent per DAU (34.7 minutes) after its strong December, even with a tick down in January. ChatGPT and CoPilot also notches slight decreases. Perplexity’ rose 34.3% from November to December and had another nice month-over-month gain in January, to the tune of 30.6%. Surprisingly Microsoft CoPilot (the consumer version) has an average time of 27.2 minutes per DAU, good for 2nd best in our list.

Editor’s Note: This post was originally published on Apptopia blog and is republished here with permission. The Apptopia team has confirmed to DIW that it was not written using AI.

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• News sites are locking out the Internet Archive to stop AI crawling. Is the ‘open web’ closing?

• Nursing professionals call for clearer AI policies as AI use in their clinics increases
by External Contributor via Digital Information World

Nursing professionals call for clearer AI policies as AI use in their clinics increases

By Destinie Wallis. Edited by Asim BN.

Artificial Intelligence (AI) has been changing healthcare, and recent research from Arkansas State University shows most nurses agree with the changes, but the same questionnaire shows many nurses fear there are no adequate protections for either them or their patients. Therefore, the enthusiasm for AI is tempered by fear. This suggests that healthcare organizations must act quickly to change their policies and education programs, which are the front lines for patient care.

Arkansas State University completed a study titled "Nurses and the AI Policy Gap: How Education Can Bridge Safety and Innovation." The study consisted of a survey of 135 registered nurses on how artificial intelligence was changing their everyday work. The results of the study show that the clinical environment in which nurses work is evolving with increasing use of technology, and, at the same time, an equally large gap in the guidelines, trust, and accountability.

AI is being adopted rapidly in nursing without the correct support in place

Most of the nurses who participated in the study (80%) stated that they used AI tools in some aspect of patient care, and more than 25% of those nurses stated they use AI tools daily. The types of clinical applications that are being supported by AI tools include:

• Charting: 61% stated they used AI-assisted charting, which is currently the largest application of AI in nursing.
• Predictive alerts: 38% stated they used predictive models to alert them to possible patient deterioration before it occurs.
• Diagnosis: 36% stated they use AI to aid in the diagnosis of patient conditions.
• Monitoring: 30% stated they use AI to monitor patients remotely.
• Bots for triage and intake: 26% stated they used AI-based bots for intake and triage.

Although 50% of the respondents stated they believed that their employer had clearly defined policies regarding the use of AI, and although over 60% of the respondents stated that they believed they would have legal protection if an AI system contributed to patient harm, the lack of clear policies and legal protection represents a serious risk to patient safety and could represent a legal liability.

Legal and ethical uncertainties are limiting AI use

The rapid adoption of AI in healthcare has moved much faster than the development of regulatory frameworks to support the safe and effective use of AI. Although many nurses believe that AI has the potential to improve efficiency and decision making in patient care, many nurses also have major concerns with the ethical and legal implications of AI.

Major AI concerns include:

• Patient harm: 63%
• Data breach: 51%
• Legal protection for nurses if an AI system contributes to patient harm: 49%
• Dependence on automation: 48%

Over one-third of the respondents stated that they have avoided using certain features of AI systems because of concerns related to the law or patient safety.

The lack of regulatory frameworks for AI is additionally demonstrated through the fact that less than thirty percent of the respondents thought that current law adequately protected patients from AI related risk, and 45% of the respondents disagreed. These concerns are not theoretical. Algorithmic bias, data security breaches, and accountability issues have yet to be resolved, and therefore nurses are increasingly being asked to use systems that may pose unforeseen risks to either themselves or their patients.

Education must fill the gap for responsible AI use

Educational resources for teaching nurses about AI systems are inadequate. Only approximately half of the responding nurses reported receiving formal training from their employer regarding the use of AI. A significant number of the nurses reported learning about AI through experience (approximately 20%), peer learning (approximately 20%), vendor training (approximately 6%), and no training (approximately 3%).

These varying levels of training lead to anxiety among the nurses regarding the use of AI systems, since only approximately 31% of the nurses felt "very comfortable" with AI systems, and the remainder of the nurses reported they are still adjusting to AI systems. Therefore, we have a workforce that is aware of technology but lacks the knowledge needed to safely and effectively utilize AI systems in high-risk situations.

Educating nurses for responsible AI use

The survey respondents were strong advocates for a variety of approaches to support their use of AI in patient care, including the development of clearly defined policies, laws, and regulations. As such, this represents a clear call for change that also provides an excellent opportunity for nurse educators to assume leadership roles in providing the necessary educational content.

To appropriately utilize AI in the delivery of patient care, nurses will need to possess education in digital literacy (to effectively evaluate information) and in ethics (the potential ethical implications of utilizing AI systems). Additionally, a nurse educator could provide examples of how AI can assist with (and supplement) a nurse’s decision making process in practice as opposed to replacing it.

Some key strategies for nurse educators include:

• Understanding AI systems: Students need to learn about the evaluation of the accuracy of an AI system, the consistency of an AI system, and the likelihood of an AI system failing. In addition, students need to have an awareness of the possibility of an AI system having bias.
• Ethics of AI: Students need to learn about the ethics of AI (such as privacy, consent, and transparency of AI algorithms).
• Responsible use of AI: Faculty can provide students with examples of how to responsibly use AI systems. Faculty can share with students their own experiences with both success and failure in implementing AI into clinical practice.

Interdisciplinary collaboration amongst health care organizations and academic institutions

Health care organizations and academic institutions must establish guidelines and standards for this type of collaboration and education. These standards must be established through consultation and emphasis on transparency, accountability and equity, as the algorithms used in AI reasoning contain embedded social and racial biases.

Warning to health care organizations and policy makers

There is a clear interest from health care organizations to advance innovation in their field. However, the lack of support necessary to take this next step in development has been identified within this study. As the nursing profession is open to the idea of AI, they do need a framework of understanding, legal guidelines and limits to incorporate AI into nursing practice.

Failure to address these concerns can have far-reaching consequences that affect patient safety, as well as the trust of the clinicians working within these organizations, when these organizations fail to provide adequate support in incorporating AI technology into the clinicians’ workload. The advancement in technology requires the advancement in the education and policy fields. Nurses need AI, but cannot utilize AI without support.

About author: Destinie Wallis has been working in the tech space for nearly ten years and focuses her attention on how new technologies like AI are transforming industries, workflows, and everyday decision-making.

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• News sites are locking out the Internet Archive to stop AI crawling. Is the ‘open web’ closing?

• How Do Algorithms Work? Experts at Status Labs Weigh In

by External Contributor via Digital Information World

News sites are locking out the Internet Archive to stop AI crawling. Is the ‘open web’ closing?

Tai Neilson, Macquarie University

Screenshot: DIW

When the World Wide Web went live in the early 1990s, its founders hoped it would be a space for anyone to share information and collaborate. But today, the free and open web is shrinking.

The Internet Archive has been recording the history of the internet and making it available to the public through its Wayback Machine since 1996. Now, some of the world’s biggest news outlets are blocking the archive’s access to their pages.

Major publishers – including The Guardian, The New York Times, the Financial Times, and USA Today – have confirmed they’re ending the Internet Archive’s access to their content.

While publishers say they support the archive’s preservation mission, they argue unrestricted access creates unintended consequences, exposing journalism to AI crawlers and members of the public trying to skirt their paywalls.

Yet, publishers don’t simply want to lock out AI crawlers. Rather, they want to sell their content to data-hungry tech companies. Their back catalogues of news, books and other media have become a hot commodity as data to train AI systems.

Robot readers

Generative AI systems such as ChatGPT, Copilot and Gemini require access to large archives of content (such as media content, books, art and academic research) for training and to answer user prompts.

Publishers claim technology companies have accessed a lot of this content for free and without the consent of copyright owners. Some began taking tech companies to court, claiming they had stolen their intellectual property. High-profile examples include The New York Times’ case against ChatGPT’s parent company OpenAI and News Corp’s lawsuit against Perplexity AI.

Old news, new money

In response, some tech companies have struck deals to pay for access to publishers’ content. NewsCorp’s contract with OpenAI is reportedly worth more than US$250 million over five years.

Similar deals have been struck between academic publishers and tech companies. Publishing houses such as Taylor & Francis and Elsevier have come under scrutiny in the past for locking publicly funded research behind commercial paywalls.

Now, Taylor & Francis has signed a US$10 million nonexclusive deal with Microsoft granting the company access to over 3,000 journals.

Publishers are also using technology to stop unwanted AI bots accessing their content, including the crawlers used by the Internet Archive to record internet history. News publishers have referred to the Internet Archive as a “back door” to their catalogues, allowing unscrupulous tech companies to continue scraping their content.

The cost of making news free

The Wayback Machine has also been used by members of the public to avoid newspaper paywalls. Understandably, media outlets want readers to pay for news.

News is a business, and its advertising revenue model has come under increasing pressure from the same tech companies using news content for AI training and retrieval. But this comes at the expense of public access to credible information.

When newspapers first started moving their content online and making it free to the public in the late 1990s, they contributed to the ethos of sharing and collaboration on the early web.

In hindsight, however, one commentator called free access the “original sin” of online news. The public became accustomed to getting their digital editions for free, and as online business models shifted, many mid- and small-sized news companies struggled to fund their operations.

The opposite approach – placing all commercial news behind paywalls – has its own problems. As news publishers move to subscription-only models, people have to juggle multiple expensive subscriptions or limit their news appetite. Otherwise, they’re left with whatever news remains online for free or is served up by social media algorithms. The result is a more closed, commercial internet.

This isn’t the first time that the Internet Archive has been in the crosshairs of publishers, as the organisation was previously sued and found to be in breach of copyright through its Open Library project.

The past and future of the internet

The Wayback Machine has served as a public record of the web for more than three decades, used by researchers, educators, journalists and amateur internet historians.

Blocking its access to international newspapers of note will leave significant holes in the public record of the internet.

Today, you can use the Wayback Machine to see The New York Times’ front page from June 1997: the first time the Internet Archive crawled the newspaper’s website. In another 30 years, internet researchers and curious members of the public won’t have access to today’s front page, even if the Internet Archive is still around.

Today’s websites become tomorrow’s historical records. Without the preservation efforts of not-for-profit organisations like The Internet Archive, we risk losing vital records.

Despite the actions of commercial publishers and emerging challenges of AI, not-for-profit organisations such as the Internet Archive and Wikipedia aim to keep the dream of an open, collaborative and transparent internet alive.

Tai Neilson, Senior Lecturer in Media, Macquarie University

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

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• How Do Algorithms Work? Experts at Status Labs Weigh In

by External Contributor via Digital Information World

How Do Algorithms Work? Experts at Status Labs Weigh In

Written by Status Labs. Edited by Asim BN.

Algorithms shape nearly every aspect of our digital lives. From the content that appears in your social media feeds to the search results you see on Google, these invisible systems are constantly working behind the scenes to curate, organize, and personalize your online experience. But what exactly is an algorithm, and how does it determine what you see online?

To understand the mechanics behind these powerful digital tools, the reputation management experts at Status Labs, a leading digital reputation management firm with offices across Austin, New York, Los Angeles, Miami, London, and Hamburg, explain how these systems work. Their team has spent years helping Fortune 500 companies and high-profile executives navigate the complexities of search engine algorithms and social media platforms.

What Is an Algorithm? Breaking Down the Basics

At its core, an algorithm is a set of instructions designed to perform a specific task or solve a particular problem. When algorithms are discussed in the digital context, they’re generally coded formulas within software that, when triggered, prompt technology to take relevant action.

The concept itself is straightforward: define what you need a computer to do, specify what information it needs to consider, establish the goal, and then let the system process the data according to those instructions. The computer takes in relevant information and follows the specified steps to complete the task.

What makes modern algorithms particularly sophisticated is their ability to learn. Algorithms today don't always need to lay out step-by-step plans. Instead, they can be designed to allow computers to learn over time through pattern recognition or through the integration of AI learning and reasoning into the process.

Where You Encounter Algorithms Daily

If you spend any time online, you're constantly interacting with algorithms. The digital strategy specialists at Status Labs point out that algorithms are used for organization, calculation, data processing, and automated reasoning across virtually every platform you use.

Consider these common examples:

• The movies Netflix recommends based on your viewing history
• The videos TikTok suggests in your feed
• The advertisements displayed across various apps and websites
• The search results Google serves when you type in a query

These algorithms analyze your data to find patterns in what you click on and engage with. If you tend to click on certain types of content, algorithms learn to show you more of the same. Every piece of information you provide helps these systems determine what to display to keep you engaged.

• Also read: Many Americans Unaware AI Powers Everyday Phone Features Like Weather Alerts and Call Screening

How Google's Algorithm Determines Search Rankings

Google's search algorithm represents one of the most complex and consequential algorithmic systems in existence. The search giant processes hundreds of billions of web pages to deliver what it determines are the most relevant results for any given query.

According to Google's own documentation, the company's ranking systems look at many factors and signals, including the words of your query, relevance and usability of pages, expertise of sources, and your location and settings. The goal is to present the most useful information in a fraction of a second.

Here are some key factors that Google considers when ranking web pages:

Intent and Relevance: The algorithm assesses whether your page content matches what users are actually searching for. This goes beyond simple keyword matching. The context and tone of your content can determine whether your website appears for a particular query.

Quality Content: Google prioritizes content that is unique, informative, and provides genuine value to users. Fluffy, repetitive, or spammy content will not rank well. Experienced marketers emphasize that quality content is becoming increasingly important in how search engines evaluate web pages.

User Experience: This encompasses the technical aspects of your website, including page speed, mobile-friendliness, layout accessibility, and overall site structure. Google wants users to have positive experiences when they click on search results.

Expertise and Trust: Google's systems aim to surface content that demonstrates expertise, authoritativeness, and trustworthiness. One way the algorithm assesses this is by examining whether other prominent websites link to or reference the content.

Why Google Keeps Its Algorithm Secret

Google updates its algorithm multiple times each year, and the company maintains significant secrecy around the specifics of these changes. There are three primary reasons for this approach.

First, transparency would compromise Google's competitive advantage. With over 90% of the search engine market share, revealing the exact mechanics of its algorithm would make it easier for competitors to replicate key features.

Second, algorithms require constant refinement to become more efficient and sophisticated. The central goal of Google search is delivering valuable, relevant results, which requires ongoing adjustments based on user behavior, technological trends, and evolving search patterns.

Third, too much transparency would invite manipulation. While search engine optimization is expected and encouraged, excessive knowledge about algorithmic specifics could lead people to artificially manipulate rankings, undermining Google's mission to provide users with the best possible information.

Social Media Algorithms: Engagement as Currency

Social media platforms employ their own algorithmic systems, though these operate somewhat differently from search engines. Social media algorithms focus primarily on showing users content in an organized and customized way to maximize time spent on the platform.

Each platform has its own approach. According to industry research, Facebook uses a four-step process that considers inventory (content from friends and pages you follow), signals (who posted, when it was posted, your internet speed), predictions (likelihood of engagement), and relevance scoring.

TikTok's algorithm has become particularly notable for its ability to surface content users didn't know they wanted. The platform analyzes what videos you've watched, what you've liked, video popularity, matching tags, and contextual factors like location and language preferences.

LinkedIn takes a different approach, emphasizing content that delivers professional insights. The platform rewards posts offering ideas, insights, and inspiration while favoring authentic, substantive conversations over quick-hit content.

The Role of Machine Learning in Modern Algorithms

Today's algorithms increasingly incorporate machine learning capabilities, allowing them to improve automatically through experience. Pattern recognition algorithms can now identify regularities in data and use those patterns to make predictions or classifications.

This technology powers everything from spam filters that learn to identify unwanted emails to recommendation systems that suggest products based on your browsing history. The algorithm examines data, identifies relevant features, extracts insights, and then implements those learnings in practice.

Machine learning has made algorithms significantly more sophisticated. Rather than following rigid rules, these systems can adapt to new patterns and improve their accuracy over time. This is why platforms like TikTok can seem almost prescient in understanding user preferences, sometimes surfacing content that users didn't even realize they'd enjoy.

Why Understanding Algorithms Matters

For business owners, marketers, and anyone looking to establish a strong online presence, understanding how algorithms work is essential. Learning what search engines and social media platforms prioritize can help identify areas for improvement in your digital strategy.

For individual users, algorithm literacy enables more empowered online participation. As more of our lives move online, understanding why certain content appears in your feed helps you evaluate the context and value of what you're seeing. This awareness makes you a more educated consumer of digital information.

In summary: the best way to operate in a system is to understand how that system works. Algorithms will only become more sophisticated over time, making foundational knowledge increasingly valuable for anyone navigating the digital landscape.

Practical Implications for Your Online Reputation

Understanding algorithms has direct implications for managing your online reputation. Status Labs, which has helped clients across more than 40 countries with their digital presence, notes that algorithmic knowledge allows businesses and individuals to:

• Create content more likely to rank well in search results
• Understand why certain information appears prominently when someone searches for you or your company
• Develop strategies for improving what shows up in search results
• Make informed decisions about social media engagement and content creation

The connection between Status Labs' expertise in reputation management and algorithmic understanding is direct. Controlling your online narrative requires knowing how platforms decide what to display, when, and to whom.

The Future of Algorithmic Systems

Algorithms are becoming more complex as artificial intelligence capabilities expand. The integration of large language models and advanced machine learning means that future algorithms will likely be even better at understanding context, intent, and user preferences.

This evolution presents both opportunities and challenges. More sophisticated algorithms can deliver more relevant, personalized experiences. However, they also raise important questions about transparency, fairness, and the concentration of power in the hands of platforms that control these systems.

For businesses and individuals alike, staying informed about algorithmic developments remains crucial. The digital landscape continues to evolve, and those who understand the underlying systems will be better positioned to navigate it successfully.

Whether you're trying to improve your company's search visibility, understand why certain content appears in your feeds, or simply become a more informed digital citizen, algorithmic literacy provides a valuable perspective on the invisible systems shaping your online experience every day.

Image: Customer experience creative collage / freepik

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by External Contributor via Digital Information World

Facial recognition technology used by police is now very accurate – but public understanding lags behind

Kay Ritchie, University of Lincoln and Katie Gray, University of Reading

Image: Alex Borland/ Publicdomainpictures. License: CC0 Public Domain 

The UK government’s proposed reforms to policing in England and Wales signal an increase in the use of facial recognition technology. The number of live facial recognition vans is set to rise from ten to 50, making them available to every police force in both countries.

The plan pledges £26 million for a national facial recognition system, and £11.6 million on live facial recognition technology. The announcement has come before the end of the government’s 12-week public consultation on police use of such technology.

The home secretary, Shabana Mahmood, claims facial recognition technology has “already led to 1,700 arrests in the Met [police force] alone – I think it’s got huge potential.”

We have been researching public attitudes to the use of this technology around the world since 2020. While accuracy levels are constantly evolving, we have found people’s awareness of this is not always up to date.

In the UK, the technology has so far been used by police in three main ways. All UK forces have the capability to use “retrospective” facial recognition for analysis of images captured from CCTV – for example, to identify suspects. Thirteen of the 43 forces also use live facial recognition in public spaces to locate wanted or missing individuals.

In addition, two forces (South Wales and Gwent) use “operator-initiated facial recognition” through a mobile app, enabling officers to take a photo when they stop someone and then compare their identity against a watchlist containing information about people of interest – either because they have committed a crime or are missing.

In countries such as China, facial recognition technology has been used more widely by the police – for example, by integrating it into realtime mass surveillance systems. In the UK, some private companies including high-street shops use facial recognition technology to identify repeat shoplifters, for example.

Despite this widespread use of the technology, our latest survey of public attitudes in England and Wales (yet to be peer reviewed) finds that only around 10% of people feel confident that they know a lot about how and when this technology is used. This is still a jump from our 2020 study, though, when many of our UK focus group participants said they thought the technology was just sci-fi – “something that only exists in the movies”.

A longstanding concern has been the issue of facial recognition being less accurate when used to identify non-white faces. However, our research and other tests suggest this is not the case with the systems now being used in the UK, US and some other countries.

How accurate is today’s technology?

It’s a common misconception that facial recognition technology captures and stores an image of your face. In fact, it creates a digital representation of the face in numbers. This representation is then compared with digital representations of known faces to determine the degree of similarity between them.

In recent years, we have seen a rapid improvement in the performance of facial recognition algorithms through the use of “deep convolutional neural networks” – artificial networks consisting of multiple layers, designed to mimic a human brain.

Surrey and Sussex police forces unveil new live facial recognition vans, November 2025. Video: Sussex Police.

There are two types of mistake a facial recognition algorithm can make: “false negatives”, where it doesn’t recognise a wanted person, and “false positives” where it incorrectly identifies the wrong person.

The US National Institute of Standards and Technology (Nist) runs the world’s gold standard evaluation of facial recognition algorithms. The 16 algorithms currently topping its leaderboard all show overall false negative rates of less than 1%, while false positives are held at 0.3%.

The UK’s National Physical Laboratory’s data shows the system being tested and used by UK police to search their databases returns the correct identity in 99% of cases. This accuracy level is achieved by balancing high true identification rates with low false positive rates.

While some people are uncomfortable with even small error rates, human observers have been found to make far more mistakes when doing the same kinds of tasks. Two of the standard tests of face matching ask people to compare two images side-by-side and decide whether they show the same person. One test recorded an error rate of up to 32.5%, and the other an error rate of 34%.

Historically, when testing the accuracy of facial recognition technology, bigger error rates have been found with non-white faces. In a 2018 study, for example, error rates for darker-skinned women were 40 times higher than for white men.

These earlier systems were trained on small numbers of images, mostly white male faces. Recent systems have been trained on much larger, deliberately balanced image sets. They are actively tested for demographic biases and are tuned to minimise errors.

Nist has published tests showing that although the leading algorithms still have slightly higher false positive rates for non-white faces compared with white faces, these error rates are below 0.5%.

How the public feel about this technology

According to our January 2026 survey of 1,001 people across England and Wales, almost 80% of people now feel “comfortable” with police using facial recognition technology to search for people on police watchlists.

However, only around 55% said they trust the police to use facial recognition responsibly. This compares with 79% and 63% when we asked the same questions to 1,107 people throughout the UK in 2020.

Both times, we asked to what extent people agree with police using facial recognition technology for different uses. Our results show the public remains particularly supportive of police use of facial recognition in criminal investigations (90% in 2020 and 89% in 2026), to search for missing persons (86% up to 89%), and for people who have committed a crime (90% down slightly to 89%).

There are lots of examples of facial recognition’s role in helping police to locate wanted and vulnerable people. But as facial recognition technology is more widely adopted, our research suggests the police and Home Office need to do more to make sure the public are informed about how it is – and isn’t – being used.

We also suggest the proposed new legal framework should apply to all users of facial recognition, not just the police. If not, public trust in the police’s use of this technology could be undermined by other users’ less responsible actions.

It is critical that the police are using up-to-date systems to guard against demographic biases. A more streamlined national police service, as laid out in the government’s latest white paper, could help ensure the same systems are being used everywhere – and that officers are being trained consistently in how to use these systems correctly and fairly.

Kay Ritchie, Associate Professor in Cognitive Psychology, University of Lincoln and Katie Gray, Associate Professor, School of Psychology and Clinical Language Sciences, University of Reading

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

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by External Contributor via Digital Information World