Eroding a virtue: AI trains people to expect instant answers – and that’s bad news for patience

Christian B. Miller, Wake Forest University

Image: Thirdman - Pexels

When I was growing up, teachers would assign research papers that required going to the library, or later, searching for relevant material on the internet. If the paper was going to turn out well, we students needed to patiently comb through piles of material, weaving what we found into a coherent argument that was well-supported with evidence.

Unbeknownst to us at the time, our teachers were giving us a chance to develop our patience.

That chance is rapidly disappearing with increased use of artificial intelligence tools. Now you can have an AI do everything from school assignments to legal writing, sermon preparation, vacation planning, work emails and academic research. Researchers are already documenting how using AI tools in these contexts likely erodes critical thinking skills.

But what hasn’t been appreciated is AI’s effect on patience. As a philosopher who has written extensively about virtue, including the virtue of patience, I am especially concerned about what people can do to resist this trend.

What is patience, and why is it important?

Patience involves responding calmly when it is taking longer than you want to accomplish your goals.

When I am stuck in a traffic jam, or the checkout line is barely moving, I might wish that I was meeting my goals faster, but my calm demeanor is a sign that I am being patient. If I react to delays like these with frustration or anger, that is a sign that I am being impatient.

The same applies in the case of doing research. If it is taking me awhile to find everything I need, that can test my patience. But if I react to such a delay with calmness, I avoid frustration or anger and hence impatience.

Philosophers, theologians and educators have long considered patience an important character trait to cultivate. It is a virtue that contributes to well-being. More specifically, researchers have linked it to a variety of good outcomes, including healthier lifestyles, greater emotion regulation, more fulfilling relationships, increased caring about equity and justice, increased cooperation, greater purpose in life, lower depression and higher life satisfaction.

Why AI tools erode a capacity for patience

AI tools are helping foster a culture of immediacy, thereby diminishing the capacity for patience. Admittedly, we already started down this path with the dawn of the internet and the launch of fast and easy search engines. But now, AI instantaneously delivers fully developed answers, further reducing the delays once experienced as people searched, assessed and integrated information from various sources.

The training in patience that people used to get from thorough research and investigation is being replaced by a growing sense of impatience with thinking that takes time and effort. And this impatience doesn’t just stop with research. It extends to writing as well.

Research on AI and patience is still in its infancy. But my conclusions about these impacts rest on plausible inferences from what researchers know more generally about cognitive psychology. For instance, psychologists have long understood that people’s expectations change due to repeated use and exposure to something.

This adaptation explains why the hourlong train ride to work can start out as exhausting, but become part of your daily routine. Or you might initially be impressed by how fast your new computer is, but after a while you take it for granted and get frustrated if loading a PowerPoint presentation takes even a few moments.

Hence using AI tools is likely to recalibrate what feels normal to you. In particular, it is likely to normalize getting immediate, fully formed answers to your questions. This shift, I contend, makes people increasingly impatient with the very tasks of research and investigation that helped train us to become more patient in the past.

One concrete illustration of this change is with students. If a professor gives an assignment involving interpreting an author’s text and then developing a critique of the author’s position, students today are very tempted to offload the patient work of interpretation and critique to an AI.

Or consider sermon preparation. Pastors normally take hours a week to examine the original language for their text, consult commentaries, develop illustrations and examples, and deliberate about practical applications. Now, this process can all be done in a matter of seconds using AI, and one study found that a majority of pastors are using it for sermon preparation. There is no patience training happening here.

What can be done?

There are ways to cultivate patience in the age of AI tools, but they will not be easy. Here are three:

• Deliberately choose a slower path. Select this option because it comes with intellectual struggles, not in spite of them. Don’t rely on AI summaries or shortcuts, but try to come up with the answers on your own. This choice needs to be deliberative since the default human tendency is to take the easier route. But the long-term benefit is worth the short-term cost.

• Design your environment. Remove AI tools from your surroundings and carve out dedicated time free of distractions and notifications. Reading and writing take time, and by being willing to invest that time and not get impatient with how long it is taking, you can cultivate patience.

• Encourage and reward intellectual engagement. Institutions such as schools and churches have a structural role to play. The more such institutions can resist integrating AI tools into every aspect of their operations, and instead incentivize human intellectual engagement even at the expense of efficiency, the better as far as patience is concerned.

There is one other hopeful suggestion. Patience can be developed in lots of different areas of life that have nothing to do with research and which are less susceptible to AI incursion. Working on a craft project, detailing a car, weeding a garden, practicing your basketball shot, lifting weights – all these activities can foster patience too. The more this character muscle is strengthened, the more it will be available to use in many different areas of your life.

Christian B. Miller, Professor of Philosophy, Wake Forest 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

The Quiet Advantage in Hiring Right Now: Practicing Your Interview Like It’s a Skill (Ad)

Most people prepare for interviews the same way: skim the job description, reread their resume, maybe watch a couple of videos, and hope the conversation goes their way. The problem is that interviews aren’t just “knowledge checks”—they’re performance moments. And like any performance, the difference is usually rehearsal.

If you’ve been looking for a simple way to pressure-test your answers before you’re in front of a real hiring manager, an Interview Practice Tool can be a practical starting point. Not because it magically makes you a better candidate, but because it helps you hear yourself, spot gaps, and tighten your story before it counts.

Why Interviews Feel Hard (Even When You’re Qualified)

Interview anxiety is often framed as nerves, but it’s usually something more specific: uncertainty. You don’t know what they’ll ask, how your answers will land, or whether you’re giving “too much” detail or not enough. That uncertainty is what makes even smart, experienced people suddenly ramble.

There’s also a mismatch between how we think and how interviews work. In real life, you solve problems over time, with context. In an interview, you’re expected to compress that into clean, confident stories—on demand.

Image: Edmond Dantès - pexels

The most common “qualified but shaky” moments

• Over-explaining because you’re trying to prove you know your work.
• Underselling because you assume results “speak for themselves.”
• Blanking when asked about conflict, failure, or salary expectations.
• Drifting off-topic when a question triggers a long timeline.

Practice doesn’t eliminate these risks, but it makes them visible. Once you can understand your own patterns, you can actually change them.

What Career Tools Platforms Get Right: Feedback Loops

The best career tools platforms don’t just hand you templates and generic tips. They create feedback loops—small, repeatable ways to improve. That can be resume checks, cover letter support, or interview practice that lets you rehearse answers and refine them quickly.

Think about how product teams work: they don’t ship once, they iterate. Your interview answers should work the same way. A platform that encourages iteration helps you move from “I think this sounds okay” to “I know this is clear.”

A simple example: turning a vague answer into a strong one

Here’s the difference practice can make with a question like: “Tell me about a time you handled a tough deadline.”

Vague: “I’ve dealt with tight deadlines a lot. I just prioritize and get it done.”

Stronger: “In my last role, a client moved a launch date up by two weeks. I mapped tasks by risk, cut non-essential scope, and set daily check-ins with design and QA. We launched on time, and support tickets dropped 18% compared to the previous release.”

The second answer isn’t “better” because it’s longer—it’s better because it’s specific, structured, and measurable. That’s what a feedback loop trains you to do.

Is ResumeCoach Recommended? A Practical Way to Think About Credibility

When people ask whether a platform is worth using, they’re usually asking two questions at once: “Will it help me?” and “Can I trust it with my time and data?” For interview practice specifically, usefulness is tied to how well the tool mirrors real interview pressure and whether it helps you improve, not just “perform.”

ResumeCoach positions itself as a career tools platform that includes interview practice support alongside other job-search essentials. If you’re evaluating whether it’s a fit, focus less on promises and more on whether the workflow matches how you actually prepare.

What to look for before you commit time to any interview tool

• Realistic prompts that reflect the roles you’re applying for, not only generic questions.
• Repeatability so you can practice the same competency answer until it’s crisp.
• Actionable feedback that helps you adjust structure, clarity, and relevance.
• Time efficiency so practice sessions can fit into a normal week.

If you finish a session with a clearer “next draft” of your answers, the tool is doing its job.

Is ResumeCoach Safe and Legit? The Common-Sense Checklist

Any time you’re using career tools online, it’s reasonable to ask about safety and legitimacy—especially when you’re uploading resumes or sharing details about your work history. While you should always review a service’s policies directly, there are a few practical checks that help you make smarter decisions quickly.

A quick checklist for evaluating safety

1. Know what you’re sharing. For interview practice, you can often avoid sensitive details (client names, internal metrics, private project info) and still tell a strong story.
2. Scan privacy and data handling basics. Look for clear language about what is stored, for how long, and whether content is used beyond providing the service.
3. Use role-appropriate anonymity. Replace identifying details with “a retail client” or “a healthcare partner” without weakening your example.
4. Keep your own copy. Save your best answers separately so you’re not locked into one workflow.

Legitimacy, in practice, comes down to transparency and outcomes. If a platform clearly explains what it does and you can measure your improvement after a few sessions, you’re on solid ground.

How to Practice Without Sounding Scripted

The biggest fear people have about practicing is sounding “rehearsed.” It’s a valid concern—nobody wants to come off like they memorized a speech. But the goal isn’t memorization. It’s building a reliable structure so your best examples show up when you need them.

Try this three-part structure (it keeps you natural)

• Context: What was happening, and what was at stake?
• Choice: What did you decide to do, and why?
• Change: What improved, and how do you know?

Once you have that skeleton, you can vary the wording every time. That’s what keeps it human while still being clear.

“The best interviews don’t sound perfect. They sound prepared—clear enough to follow, specific enough to trust.”

Closing: Treat the Interview Like a Deliverable

If you’re already doing the work—building skills, shipping projects, solving problems—then the interview is simply the moment you translate that value for someone who hasn’t seen it yet. Career tools platforms can help, but only when you use them as a practice loop, not a shortcut.

Your next step is straightforward: pick three questions you dread, practice them until your answers are specific and measurable, and pressure-test them with a tool or a friend. When the real interview comes, you won’t be “hoping” you sound confident—you’ll recognize your own story and deliver it cleanly.

Edited by Irfan Ahmad.

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Filtering out humanity: AI-assisted internet research favors cold logic over ethos and pathos

By David Danelski, UC Riverside News

Image: Amirhosain Gazor - unsplash

Is the internet losing its soul? A collaborative study by UC Riverside computer and social scientists suggests so. As artificial intelligence increasingly answers our online questions with quick summaries and polished explanations, we may be gaining efficiency while losing something distinctly human in the process.

The study found that large language models, or LLMs, such as ChatGPT and Gemini overwhelmingly rely on logic and factual consistency when responding to subjective questions, while web pages written by humans draw from a richer mix of reasoning that includes emotion, lived experience, ethics, and personal authority.

“As people increasingly rely on AI systems for information discovery at the expense of traditional web searches, the web may gradually lose its soul and cease to reflect the human nature that has shaped it over the past 25 years,” said co-author Vagelis Hristidis, a computer scientist at UCR’s Bourns College of Engineering. “This may give rise to new information dissemination platforms in the future.”

Another co-author, Kevin Esterling, a professor of public policy and political science, added, “As humans, we’re hardwired to think that anything producing language has human cognition behind it, but this paper is showing that machines produce language that doesn’t have human qualities when it comes to reasoning and argumentation.”

The study compared how AI systems and traditional web searches respond to controversial or opinion-based questions, such as whether governments should ban fossil-fuel cars or whether the U.S. healthcare system needs major reform. It was presented this week at the ACM Web Science Conference in Braunschweig, Germany, by UCR computer science doctoral student and lead author Md Taukir Azam Chowdhury.

To conduct the study, the researchers analyzed responses from ChatGPT and Gemini alongside results from Google and Bing web searches. Using hundreds of subjective questions drawn from established research datasets, they examined not only the positions taken by the systems, but also the types of justifications used to support those positions.

The researchers classified reasoning according to Aristotle’s rhetorical triangle: logos, ethos, and pathos. Logos refers to logic and factual consistency. Ethos appeals to authority or credibility. Pathos appeals to emotions and shared human experience.

“What we found is that humans essentially use all three of those, whereas LLMs essentially only rely on logos,” Esterling said. “The way they try to persuade is different from the way humans persuade.”

Esterling said the difference becomes obvious in everyday searches.

Suppose someone wants a margarita recipe. An AI chatbot may instantly produce a competent recipe distilled from massive amounts of training data. But by bypassing culinary blogs and personal stories, users also miss the details that could make mixing and consuming the drink more memorable and rewarding.

The website Difford’s Guide, for example, offers dozens of margarita recipes divided into seven styles, including classic, fruity, floral, herbal, and spicy. Written by Simon Difford, the site also provides a history of the cocktail, tracing it back to a journalist’s discovery in the 1930s of a drink then called the “Tequila Daisy” during his travels in Mexico. (Margarita is the Spanish word for daisy.) He further documents with a 1936 newspaper report how it was created unwittingly by an Irish bartender in Tijuana who had grabbed the wrong bottle while trying to mix another drink.

LLMs, however, filter out such depth, nuance, and passion that human writers bring to the table.

The study also found that web pages contain a far broader range of reasoning styles than LLMs. Human-authored web content mixed factual arguments with moral concerns, practical consequences, emotional appeals, and storytelling. For example, a call for funding food banks may draw on the writer’s experience with childhood poverty.

AI systems, by contrast, strongly favored fact-centered explanations that “prioritize logos-type reasoning,” Esterling said.

The researchers suspect this may stem partly from the “alignment” and safety systems AI companies build into their models. These guardrails are designed to steer responses toward factual, non-harmful answers and away from controversial or emotional language.

The study also found that ChatGPT and Gemini closely resembled each other in how they answered questions, even when their responses diverged significantly from the broader diversity found on the web.

Esterling said humans communicate very differently from machines because people constantly anticipate how others will react emotionally and intellectually during conversation.

“When humans talk to each other, we can understand what the other is thinking,” he said. “There’s this kind of two-way interaction.”

Large language models, however, do not truly think about the audience in that way, he said. Instead, they generate statistically probable sequences of words based on training data and internal parameters.

“It’s not like talking to a person at all,” Esterling said. “It’s just a machine that’s predicting what words ought to be said in response to a prompt.”

The researchers warn that as more people rely on AI systems instead of traditional web searches to understand politics, health, ethics, and public affairs, society could gradually lose exposure to the messy but deeply human diversity of reasoning that shapes public discourse.

The AI-based research may be faster and more efficient. Still, it is also flatter, less personal, and less connected to the emotional and moral experiences that help people understand one another, Esterling said.

Hristidis added, “When using AI platforms instead of Web searches, we retrieve a distilled version of knowledge, constrained by the guardrails of each AI platform, and missing any human emotion or opinion diversity.”

The study’s title is “Comparing the Subjective Opinions and Justifications of LLMs and Web Search Engines.” The research team included Esterling, Hristidis, Chowdhury, and doctoral students from the Computer Science and Engineering Department: Jannat Ara Meem, and Zabir Al Nazi. Chowdhury led the experiments for this work.

This article was originally published by University of California, Riverside News and has been republished here with permission.

Reviewed by Irfan Ahmad.

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UN report warns AI could soon use 3% of world’s electricity and more water than we need to drink

Amanda Turnbull-McRae, University of Waikato

Image: Field Engineer - Pexels

One argument often used to quell concerns about the rising energy and resource demand of data centres is that artificial intelligence (AI) models will need less in the future as they improve and become more efficient.

But this seemingly logical thinking is a trap, according to a new United Nations report that quantifies the environmental costs of AI.

The report estimates that by 2030, AI’s energy use could double to consume 3% of the world’s electricity, produce emissions to equal the UK and deplete more water for cooling than the annual drinking water need of the global population.

It also anticipates the use of AI will follow an economic principle known as the “Jevons paradox”, which predicts that when technological improvements increase the efficiency of a resource, it leads to a rise, rather than a fall, in the total consumption of that resource.

The paradox is named after economist William Stanley Jevons who observed this effect with the use of coal in 19th-century England. Efficiency gains did not reduce overall consumption. Instead, the lower costs resulted in expanded use and higher overall demand.

As AI models become cheaper and more attractive, the report expects this to encourage new uses and higher volumes of use, eroding and possibly erasing any savings from efficiency advances.

To avoid falling into this trap, it lays out a roadmap for responsible AI use based on guiding principles of transparency, efficiency by design, equity and justice, lifecycle responsibility, global cooperation and sustainable use.

The scale of the problem

Last year, data centres already consumed as much electricity as Saudi Arabia, which ranks as the world’s 11th largest electricity consumer.

If electricity use doubles as projected by 2030, the associated carbon footprint would require 6.7 billion trees grown over ten years to offset this demand.

Data centres would also require 9.3 trillion litres of water and land nearly ten times the size of Mexico City.

Beyond resource use, the report also underscores the structural inequity at the heart of the AI boom, with only 32 nations hosting AI-specific cloud infrastructure and 90% of that capacity located in the US and China.

It warns of a widening digital divide between nations that build and control AI systems and those that consume them, with the latter often bearing a disproportionate environmental burden caused by mineral extraction and e-waste.

Responsible AI use

Two main forces shape AI’s operational footprint: how much we use it and how we use it.

This involves all tasks AI models perform, from text and code generation to image and video. Each of these tasks requires different levels of computational effort.

The model choice also matters as each AI system performs these task with distinct energy and environmental costs.

The report argues responsible AI requires full value-chain governance, from mineral sourcing to recycling and safe disposal.

It calls for a twinning of capability and environmental stewardship – thinking about both what AI can do for us and the protection of the natural environment.

This would mean making environmental disclosures a routine part of AI development, at both the model and task level, and incorporating projected AI demand in climate and energy planning.

Responsible AI is crucial as countries are promoting and adopting AI across government and the public sector.

In Aotearoa New Zealand, the government has launched a national AI strategy and a public service AI framework.

While the framework was informed by the OECD’s values-based AI principles, including inclusive and sustainable development, there is no requirement for environmental disclosures and no regulator compiling energy use or emissions.

Likewise in Australia, improving public services is part of the national AI plan. For example, the National Film and Sound Archive of Australia has created Bowerbird, a machine learning-enabled mass audio and video transcription engine, to document material. The Department of Veteran’s Affairs has developed a proof-of-concept tool to see whether AI can help speed up the processing of claims.

Both countries take a deliberate “light touch” and principles-based regulatory approach to AI. But this approach risks overlooking the growing environmental cost of AI that can’t be solved by improving it.

The natural environment is foundational to the economy, culture and wellbeing. It should be at the centre of our thinking. It’s time to rethink the AI innovation playbook and shift focus toward a sustainable tech future.

Amanda Turnbull-McRae, Senior Lecturer in Law, University of Waikato

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

Reviewed by Irfan Ahmad.

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Fast deliveries worsen conditions for e-commerce warehouse workers

By James Dean, Cornell Chronicle

Image: Daniel Bernard - unsplash

Holding off on that late-night online order of a book, blender or blue jeans could ease the strain on a warehouse worker.

Consumers’ around-the-clock, often impulsive demand for cheap, rapidly delivered products creates harsher working conditions in e-commerce fulfillment centers than in traditional warehouses, according to Cornell-led research that provides the first comprehensive assessment of e-commerce work in the U.S.

Between Amazon and Walmart, the nation’s two largest warehouse employers, surveys found jobs at Amazon fulfillment centers to be significantly worse – more intense and dangerous – likely driven by the e-commerce market leader’s emphasis on fast delivery.

The findings are cause for concern if Amazon’s practices become the norm, the researchers said – but also show alternative approaches are viable.

“E-commerce heightens the frenzy among retailers to satisfy customers with convenience, speed and cheap prices, offerings that all erode job quality for workers behind the scenes,” said Alexander Kowalski, assistant professor of human resource studies in the ILR School. “This is a problematic trend for a large and growing share of the labor market – but it doesn’t have to be this way.”

Kowalski is the first author of “At the Mercy of the Market: E-Commerce and Warehouse Work in the United States,” published May 19 in the ILR Review. Co-authors are Sanjay Pinto and Beth Gutelius, senior research fellows at the University of Illinois-Chicago’s Center for Urban Economic Development; and Steven Vallas, professor emeritus of sociology at Northeastern University.

In related papers, Kowalski has found shortcomings in so-called “collaborative robots” being deployed in e-commerce warehouses, and that a program giving e-commerce workers more of a voice improved their well-being.

Research about the workers who enable e-commerce has highlighted reasons such jobs may be more challenging: They involve more varied inventory, more intensive labor, less predictable demand and more frequent returns. In response, workers including material movers, pickers, packers and clerks earn low wages and may face unpredictable schedules, monotonous tasks, limited opportunities for breaks, digital surveillance and discipline imposed by algorithms.

Research, however, has largely focused on Amazon, leaving it unclear whether job quality there represented warehousing broadly, the e-commerce sector or a particular corporate strategy. To address that question, Kowalski’s team first sought to establish a big-picture view of U.S. warehouse work, which has not been documented comprehensively despite enormous growth – tripling over the past 20 years, as e-commerce increased to 16.4% of retail sales.

The team surveyed about 400 hourly employees representative of the U.S. population of warehouse workers. Some supported e-commerce fulfillment centers primarily reacting to consumer orders, called business-to-consumer (B2C) warehouses; others worked in distribution and sorting centers serving brick-and-mortar stores or manufacturers, called business-to-business (B2B) warehouses.

The results confirmed a “B2C Effect”: Warehouse jobs were challenging on average, but worse at B2C – largely e-commerce – sites. Those workers experienced more pressure to move quickly and avoid taking breaks, greater exposure to unsafe situations and significantly worse overall well-being – a measure of anxiety, burnout and stress – but were not paid more.

“The warehouse segment dedicated to e-commerce offers lower-quality jobs across the board,” Kowalski said. “The results suggest that more time-sensitive pressures related to speedy delivery make for especially low-quality jobs in e-commerce.”

To investigate if those issues were consistent across retailers, the researchers next surveyed warehouse employees at Amazon (1,450 respondents) and Walmart (450 respondents), which respectively accounted for 38% and 6% of U.S. online shopping in 2023.

The results showed Amazon’s fulfillment centers housed the least desirable warehousing jobs. Respondents working in those facilities were more likely to report higher intensity, less opportunity, lower wages, greater unfairness and more challenges concerning safety, well-being and injuries. Walmart’s baseline job was not high-quality but varied little by warehouse type, whereas Amazon’s e-commerce jobs – the majority of its warehouse workforce – were significantly worse than comparable Walmart jobs, the researchers said. The findings suggest Walmart’s emphasis on low prices imposes less burden on e-commerce warehouse workers than Amazon’s focus on delivery speed.

“E-commerce creates downward pressure on work conditions,” Kowalski said, “and Amazon dials it up even more.”

The contrast between e-commerce’s biggest players, however, implies that Amazon’s lower job quality is not inevitable, but flows from its strategic priorities. Improving job quality will require worker movements, regulatory policies, consumer awareness and businesses seeing value in doing things differently, the researchers said. Kowalski pointed to his research showing better outcomes when workers are more involved in decision-making as further evidence that e-commerce can be managed less punitively.

“We are going down a path that should be worrisome for workers and for consumers alike,” he said. “But if there is a silver lining, it’s that one can arrange e-commerce operations in a way that shifts some of the burden off of workers. Now it’s up to companies to choose to do things that way.”

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

Reviewed by Irfan Ahmad.

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Powerful AI is making facial recognition better at identifying you

Vijayan Asari, University of Dayton

Image: AI-generated for illustrative purposes by DIW

If you are fortunate enough to have a ticket to an event at Madison Square Garden in New York – say, an NBA Finals game – one aspect of your visit will be having your face scanned by a facial recognition system.

Major event venues are increasingly using the technology. Some, like Madison Square Garden, use it for surveillance purposes, and some, like Citizens Bank Park in Philadelphia, to offer visitors optional ticketless admission.

Adoption of facial recognition technology is increasing, becoming more prevalent in daily life, from public buses to public buildings. The Transportation Security Administration has deployed the latest facial recognition technology at security checkpoints at numerous airports. The agency says the new system will be used in cities across the U.S. that are hosting World Cup 2026 soccer matches.

The growing use of facial recognition has broadened concerns about accuracy and bias. But in my research studying facial recognition technology in the Vision Lab at the University of Dayton, I’ve found that advanced deep learning models have made face recognition systems more accurate and reliable. The AI models, trained on hundreds of millions of face images, are more than 99% accurate in controlled environments – settings such as cellphones, airports and border checkpoints.

Facial recognition basics

Facial recognition involves three steps: locate a face in an image or video frame, create a faceprint that catalogs salient features – including the shape of the face and landmark points such as eyes, nose and mouth – and record the texture of the skin. Then it compares the faceprint to those in a database, which may be inside a smartphone or at a bank or hospital, to verify a person’s identity or allow access.

In the physical world, these systems are faster and simpler than requiring people to show IDs. In the online world, they are easier than entering a login name and password. Facial recognition also significantly reduces the possibility of forgery or fraud when compared with ID cards or passwords.

Improvements in the technology have come from a variety of research projects. FaceNet, a deep learning model developed by Google, has upgraded recognition of faces that are partly covered or hidden in images. DeepFace, a landmark AI-powered facial recognition system developed by Facebook AI Research, achieves the same high level of verification shown by humans.

NeoFace, a highly accurate AI-powered algorithm developed by NEC, is built into Mobile Fortify, the mobile facial recognition system used by U.S. Immigration and Customs Enforcement to identify people.

Reducing false positives and negatives

Real-world conditions such as poor lighting, difficult viewing angles, extreme facial expressions, concealment by face masks or sunglasses, and poor image quality can still hamper performance, leading to faulty identification. False positives and false negatives are the two primary errors. False positives are when a person is incorrectly matched to a different person in a database. False negatives are when an individual is not found in a database, even though their image exists there.

False positives are more critical in security and safety applications. They can lead to wrongful accusations, discrimination or detention. In 2025, a 50-year-old woman in Tennessee was arrested and put in jail for six months based on an AI-powered facial recognition system that incorrectly tied her to a North Dakota bank fraud investigation. False negatives may prompt authorities to deny services to people who qualify for them.

Accuracy can suffer if models are trained on data that does not reflect real-world demographics. A 2025 study showed that systems trained on public databases in which people with darker skin tones are lacking leads to lower recognition accuracy. This kind of unintentional bias in training data may lead to misidentification of women, people of color and young and old people. One report found that facial recognition systems used by 42 U.S. government agencies falsely identified African and Asian American faces 10 to 100 times more often than white faces, in some cases leading to wrongful arrests.

Accuracy also deteriorates when people are wearing heavy makeup and for young children and old people because their landmark features tend to change more quickly than adults of other ages. Balancing datasets by collecting more representative images across age, gender and ethnicity, and frequently updating databases, can improve accuracy and produce fairer results.

Adjusting images before they are sent for matching – for example, changing brightness levels – can improve accuracy, too. People squint their eyes when they are in dark or very bright light. Advanced processing software can mimic this human trait to improve the facial recognition system’s ability to extract facial features from the image.

A full face from partial data

Humans are good at identifying a person even if part of their face is covered by sunglasses or a face mask. The brain assigns more significance to the exposed details. If facial recognition programs can learn to do the same, that would reduce false positives and false negatives, including when cameras only capture part of a face.

Facial dynamics can help, too. It may be difficult for someone to instantaneously recognize a middle school friend they haven’t seen for many years, but if the old friend smiles, that change in expression can immediately improve recall.

Researchers are developing a facial recognition method for doing this, known as volumetric directional patterning. It captures the subtle movements of facial muscles, as well as eyelid blinks, in consecutive frames of a video. It tracks how facial landmarks shift over time, as well as the context in which a face is being observed, which can improve recognition accuracy.

Researchers are also creating more accurate AI-powered three-dimensional systems that can capture the precise geometry of a face, including features such as contours of the eye socket, nose and chin. This kind of work could lead to anti-spoofing techniques that prevent facial recognition systems from falling for fake faces that are generated by computers and their human operators.

Fewer mistaken identities

Setting aside questions of privacy and cybersecurity and lingering issues of bias, one thing is clear: Facial recognition technology is improving. And that promises fewer errors – and fewer of the serious consequences that come with them.

Vijayan Asari, Professor of Electrical and Computer Engineering, University of Dayton

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

Lessons from crypto are paving the way for a democratic global financial future

By Taylor & Francis

Experts argue the convergence of traditional and decentralised finance is breaking down barriers to financial access worldwide.

Image: Kamal Uddin - Unsplash

The convergence of traditional finance (TradFi) and decentralised finance (DeFi) is fundamentally reshaping global banking and democratising access to financial services.

This convergence is creating unprecedented opportunities for those previously excluded from the formal financial system, such as those living in remote regions.

According to finance experts, tokenisation – the digital representation of real-world assets on blockchain networks – is emerging as a bridge between established financial institutions and decentralised infrastructure, paving the way for a more inclusive and efficient financial ecosystem that operates at the speed of the internet.

These emerging possibilities are laid out by experts in The New Intersection of Money: Where DeFi & TradFi Converge, authored by Scarlett Sieber, Chief Strategy and Growth Officer at Money20/20, and her colleagues at the fintech events firm.

Breaking down barriers to financial access

The convergence of new and old is particularly impactful beyond developed markets.

In emerging economies where basic access to financial services remains out of reach for many, DeFi is providing essential infrastructure. The book cites the case of Hala Mahmoud Almahmoud in rural Syria, whose farm was restarted after 14 years of conflict through a cryptocurrency payment via a plastic card that instantly provided approximately 500 dollars in relief.

“DeFi shines in areas with low trust in centralised entities,” the authors note. “Digital assets evolved from experimental curiosity to essential financial infrastructure. The most profound impact of DeFi lies in continuing humanity’s oldest financial purpose: finding new routes when existing systems create too much friction.”

Tokenisation is also fundamentally altering access to investment opportunities that were once the exclusive preserve of the ultra-wealthy. Private equity investments traditionally requiring millions of dollars in minimum commitments can now be accessed for a few thousand dollars through tokenised securities.

The financial implications are substantial. Tokenised real-world assets, once considered a fringe financial experiment, are now projected to reach between $10 trillion and $16 trillion in market value by the end of this decade.

“This growth is not mere speculation but driven by a calculated migration of traditional assets like real estate, bonds, and private equity onto the blockchain,” the authors state. “that is impact. That is scale.”

Meeting consumer expectations

The convergence does not just benefit financial entrepreneurialism but the end users – the people using banking apps, making payments and spending their wages every day.

This is because the convergence of traditional banking and digital technology addresses a fundamental disconnect between consumer expectations and the limitations of traditional banking infrastructure.

Research indicates that 83% of Gen Z prefer digital-first financial services, with 78% having one bank account and 66% using mobile apps as their primary form of banking.

“New generations accustomed to instant messaging, on-demand services, and global digital platforms began to question why moving money remained slow, opaque, and expensive,” the authors observe. “The demand was not simply for cheaper transactions but for time compression – money needed to move at the speed of the internet.”

The future of finance

Industry experts expect traditional finance and decentralised finance to continue to converge in a stepwise manner, as it continues to solve long-standing problems.

“Finance is not moving onto blockchains because it wants to be modern,” the analysts explain. “It’s doing so because programmable settlement, tokenised collateral, and always-on liquidity solve problems that have existed for decades and grown intolerable at a global scale.”

By 2030, experts suggest consumers will see money will move as easily as streaming data: instant, continuous and operating year-round.

“This is not a race to declare winners or losers,” the authors say. “It is the global financial system doing what it has always done when confronted with new infrastructure: adapting, standardising, and quietly deciding what gets to last.”

The book draws on insights from Money20/20’s global platform, which brings together executives from established financial institutions, blockchain developers, DeFi protocol creators and regulatory authorities.

As regulatory frameworks mature across the world, the experts suggest that the debate about whether DeFi belongs in the formal financial system has effectively ended. The question now is how quickly institutions can adapt to capture the efficiencies that these technologies enable – and whether they can do so while maintaining the trust and stability that underpin the global financial system.

“The future of finance isn’t coming. It’s already clearing,” the authors conclude.

This post was originally published on Taylor & Francis Newsroom and republished here with permission.

Reviewed by Irfan Ahmad.

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