Tech
Study Finds Most People Can No Longer Tell AI-Generated Voices from Real Ones
A new study has found that most people can no longer distinguish between human voices and their artificial intelligence (AI)-generated counterparts, raising growing concerns about misinformation, fraud, and the ethical use of voice-cloning technologies.
The research, published in the journal PLoS One by scientists from Queen Mary University of London, revealed that participants were able to correctly identify genuine human voices only slightly more often than they could identify cloned AI voices. Out of 80 voice samples—half human and half AI-generated—participants mistook 58 percent of cloned voices for real, while 62 percent of actual human voices were correctly identified.
“The most important aspect of the research is that AI-generated voices, specifically voice clones, sound as human as recordings of real human voices,” said Dr. Nadine Lavan, lead author of the study and senior lecturer in psychology at Queen Mary University. She added that these realistic voices were created using commercially available tools, meaning anyone can produce convincing replicas without advanced technical skills or large budgets.
AI voice cloning works by analyzing vocal data to capture and reproduce unique characteristics such as tone, pitch, and rhythm. This precise imitation has made the technology increasingly popular among scammers, who use cloned voices to impersonate loved ones or public figures. According to research by the University of Portsmouth, nearly two-thirds of people over 75 have received attempted phone scams, with about 60 percent of those attempts made through voice calls.
The spread of AI-generated “deepfake” audio has also been used to mimic politicians, journalists, and celebrities, raising fears about its potential to manipulate public opinion and spread false information.
Dr. Lavan urged developers to adopt stronger ethical safeguards and work closely with policymakers. “Companies creating the technology should consult ethicists and lawmakers to address issues around voice ownership, consent, and the legal implications of cloning,” she said.
Despite its risks, researchers say the technology also has significant potential for positive impact. AI-generated voices can help restore speech to people who have lost their ability to speak or allow users to design custom voices that reflect their identity.
“This technology could transform accessibility in education, media, and communication,” Lavan noted. She highlighted examples such as AI-assisted audio learning, which has been shown to improve reading engagement among students with neurodiverse conditions like ADHD.
Lavan and her team plan to continue studying how people interact with AI-generated voices, exploring whether knowing a voice is artificial affects trust, engagement, or emotional response.
“As AI voices become part of our daily lives, understanding how we relate to them will be crucial,” she said.
Tech
Meta Apps Collect More User Data Than Other Big Tech, Study Finds
Meta’s apps collect more types of user data on average than applications from other major technology companies, according to research by cybersecurity and privacy company Surfshark.
The study analysed 171 apps available through Apple’s App Store that were developed by Meta, Google, Microsoft, Apple and Amazon. Researchers examined the types of information identified in each app’s privacy disclosures across 35 data categories.
Meta ranked highest, with its apps collecting an average of 25 out of 35 possible data types. Surfshark said this was more than three times the average recorded for some other major technology companies.
The categories examined included information such as browsing history, precise location, purchase details and other personal data that applications can collect or associate with users.
Seven of the apps identified among the most data-intensive were owned by Meta. They included Facebook, Messenger and Meta AI, as well as Meta Horizon, Meta Ads Manager, Meta Business Suite and Forum.
The findings put Meta well ahead of the other companies included in the analysis.
Google apps collected an average of 17 data types, according to Surfshark, while Amazon collected 12. Microsoft averaged eight types and Apple seven.
The research focuses on the number of data categories associated with each application rather than the volume of individual records collected from users. It also reflects information disclosed by developers through App Store privacy labels, which can vary according to how companies classify and report their data practices.
Meta operates some of the world’s most widely used digital platforms, including Facebook, Instagram, Messenger and WhatsApp. Its services rely heavily on advertising and personalised experiences, making user information an important part of its business model.
The findings are likely to renew questions about how much personal information consumers share when using popular social media and technology services.
Privacy concerns have increased as technology companies expand their use of artificial intelligence, targeted advertising and personalised recommendations. AI-powered applications can require access to additional information depending on their features and how users interact with them.
Surfshark’s analysis does not by itself establish whether any company has violated privacy laws or whether the data collected is used improperly. The number of data categories listed by an app also does not necessarily indicate how much information a particular user contributes.
However, the research highlights the differences in data collection practices among major technology companies.
The findings could encourage users to review application privacy settings and the information requested by apps before installing or continuing to use them.
As regulators and consumers place greater scrutiny on digital privacy, technology companies face increasing pressure to explain clearly what information they collect, why it is needed and how long it is retained.
Tech
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