Twitter said Wednesday it was scrapping an automated image-cropping system after its review found bias in the algorithm controlling the function.

The messaging platform said it found the algorithm delivered "unequal treatment based on demographic differences" with whites and males favored over Blacks and females, and "objectification" bias that focused on a woman's chest or legs, described as "male gaze."

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The news comes a month after <a href="https://www.wionews.com/topics/twitter">Twitter</a> said it was launching an algorithmic fairness initiative as part of an effort to reduce bias on its platform introduced by automation.

Twitter in 2018 introduced a so-called saliency algorithm to crop images, aiming to improve consistency in the size of photos in one's timeline and to focus on their most important elements.

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Rumman Chowdhury, head of Twitter's software engineering and a specialist in ethics and artificial intelligence, said the company determined after its review that it is preferable to leave the cropping decisions to users.

"We considered the tradeoffs between the speed and consistency of automated cropping with the potential risks we saw in this research," she said in a blog post.

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"One of our conclusions is that not everything on Twitter is a good candidate for an algorithm, and in this case, how to crop an image is a decision best made by people."

The announcement comes amid heightened concerns over advanced algorithms which can deliver biased results due to a lack of data on minorities or other factors.

This week, Amazon said it was extending a ban on law enforcement's use of its facial recognition technology amid fears that its flaws could amplify racial bias.

Twitter's initiative calls for "taking responsibility for our algorithmic decisions" with the aim of "equity and fairness of outcomes," according to the company.

Twitter said Wednesday it was scrapping an automated image-cropping system after its review found bias in the algorithm controlling the function. The messaging platform said it found the algorithm delivered "unequal treatment based on demographic differences" with whites and males favored over Blacks and females, and "objectification" bias that focused on a woman's chest or legs, described as "male gaze.

Twitter said Wednesday it was scrapping an automated image-cropping system after its review found bias in the algorithm controlling the function. The messaging platform said it found the algorithm delivered "unequal treatment based on demographic differences" with whites and males favored over Blacks and females, and "objectification" bias that focused on a woman's chest or legs, described as "male gaze." Also read | Facebook deploys special team as Israel-Gaza conflict spreads across social media The news comes a month after Twitter said it was launching an algorithmic fairness initiative as part of an effort to reduce bias on its platform introduced by automation. Twitter in 2018 introduced a so-called saliency algorithm to crop images, aiming to improve consistency in the size of photos in one's timeline and to focus on their most important elements. Also read | Features of 'Twitter Blue', company's upcoming paid service revealed! Rumman Chowdhury, head of Twitter's software engineering and a specialist in ethics and artificial intelligence, said the company determined after its review that it is preferable to leave the cropping decisions to users. "We considered the tradeoffs between the speed and consistency of automated cropping with the potential risks we saw in this research," she said in a blog post. Also read | Researchers develop artificial intelligence that can detect sarcasm in social media "One of our conclusions is that not everything on Twitter is a good candidate for an algorithm, and in this case, how to crop an image is a decision best made by people." The announcement comes amid heightened concerns over advanced algorithms which can deliver biased results due to a lack of data on minorities or other factors. This week, Amazon said it was extending a ban on law enforcement's use of its facial recognition technology amid fears that its flaws could amplify racial bias. Twitter's initiative calls for "taking responsibility for our algorithmic decisions" with the aim of "equity and fairness of outcomes," according to the company.

Twitter drops image-cropping algorithm after finding racial bias

Think facial recognition has gone too far as a node of edge-cutting science? You may be right, or left, and a new study suggests that facial recognition knows it!&nbsp;

Turns out, facial recognition technology can successfully predict the political stance of people based on their Facebook profile pictures. The software's algorithm exhibited accuracy in terms of a person's political beliefs which remains way higher than a personality test, or just a wild guess. 
 
Over the last few years, scientists have been able to advance facial recognition technology, with the software effectively being able to identify and track individuals.

The study was authored by Michal Kosinski, who is an associate professor at Stanford University. He told PsyPost that he has been studying privacy risks over algorithms over the last decade.&nbsp;Naturally, critics have expressed worries over the privacy safety concerns imbued into facial recognition technology.&nbsp;

Also read:&nbsp;<a href="http://13.235.153.162/technology/american-agencies-using-facial-recognition-tools-to-nab-us-capitol-rioters-356024" hreflang="en">American agencies using facial recognition tools to nab US Capitol rioters</a>

The study took into account 1,085,785 faces obtained from social media profiles. Out of these, 977,777 emerged from users in the US, UK, and Canada who had already reported their political beliefs. The other 108,018 faces came from Facebook across the US who also reported their political leaning while completing a personality test with 100 items.

To ascertain the political leanings of users, the algorithm compared the facial features of liberals and conservatives. Turns out, the software was mostly right. Just among Facebook users in the US, the accuracy hit 73 per cent.

Also read:&nbsp;<a href="http://13.235.153.162/world/alibaba-facial-recognition-tech-can-specifically-pick-out-uighur-minority-new-report-says-350398" hreflang="en">Alibaba facial recognition tech can specifically pick out Uighur minority, new report says</a>

According to Kosinski, this result yields better accuracy than humans, who can distinguish between right-leaning and left-leaning people with 55 per cent accuracy.

The study was published in Scientific Reports on January 11, 2021, titled "Facial recognition technology can expose political orientation from naturalistic facial images".

Think facial recognition has gone too far as a node of edge-cutting science? You may be right, or left, and a new study suggests that facial recognition knows it!

Think facial recognition has gone too far as a node of edge-cutting science? You may be right, or left, and a new study suggests that facial recognition knows it! Turns out, facial recognition technology can successfully predict the political stance of people based on their Facebook profile pictures. The software's algorithm exhibited accuracy in terms of a person's political beliefs which remains way higher than a personality test, or just a wild guess. Over the last few years, scientists have been able to advance facial recognition technology, with the software effectively being able to identify and track individuals. The study was authored by Michal Kosinski, who is an associate professor at Stanford University. He told PsyPost that he has been studying privacy risks over algorithms over the last decade. Naturally, critics have expressed worries over the privacy safety concerns imbued into facial recognition technology. Also read: American agencies using facial recognition tools to nab US Capitol rioters The study took into account 1,085,785 faces obtained from social media profiles. Out of these, 977,777 emerged from users in the US, UK, and Canada who had already reported their political beliefs. The other 108,018 faces came from Facebook across the US who also reported their political leaning while completing a personality test with 100 items. To ascertain the political leanings of users, the algorithm compared the facial features of liberals and conservatives. Turns out, the software was mostly right. Just among Facebook users in the US, the accuracy hit 73 per cent. Also read: Alibaba facial recognition tech can specifically pick out Uighur minority, new report says According to Kosinski, this result yields better accuracy than humans, who can distinguish between right-leaning and left-leaning people with 55 per cent accuracy. The study was published in Scientific Reports on January 11, 2021, titled "Facial recognition technology can expose political orientation from naturalistic facial images".

Facial recognition tech accurately predicts person's political beliefs: Study

Ed Miller and Mary Nguyen are Silicon Valley software developers by day, but moonlight at solving an unusually fuzzy problem.

A few years ago the pair became mesmerized, like many of us, by an Alaskan webcam broadcasting brown bears from Katmai National Park. They also happened to be seeking a project to hone their machine learning expertise.

“We thought, machine learning is really great at identifying people, what could it do for bears?” Miller said. Could artificial intelligence used for face recognition be harnessed to discern one bear face from another?

At Knight Inlet in British Columbia, Canada, Melanie Clapham was pondering the same question. Clapham, a postdoctoral researcher at the University of Victoria working with Chris Darimont of the Raincoast Conservation Foundation, was keen to explore face recognition technology as an aid to her grizzly bear studies. But her expertise was bear biology, not AI.

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Fortuitously, the four found a match on Wildlabs.net, an online broker of collaborations between technologists and conservationists. Combining their skill sets, Miller and Nguyen volunteered spare time over several years for this passion project that would eventually bear fruit, reporting the results of their experiment last week in the journal Ecology and Evolution. The project they produced, BearID, could help conservationists monitor the health of bear populations in various parts of the world, and perhaps aid work with other animals, too.

They got started by looking for other animals that had gotten the deep learning treatment.

“In typical engineering fashion, we’re always looking for a shortcut,” Miller said.

They discovered “dog hipsterizer,” a program that found the faces, eyes and noses of dogs in photos and placed rimmed glasses and mustaches on them. “That was where we started,” Nguyen said.

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Although trained on dogs, dog hipsterizer worked reasonably well on the similarly shaped faces of bears, giving them a programming head start. Nevertheless, Nguyen said, the work’s initial stages were tedious. Creating a training data set for the deep learning program involved examining over 4,000 photos with bears in them and then manually highlighting each bear’s eyes, nose and ears by drawing boxes around them so the program could learn to find these features.

The system also had to overcome a challenge of brown bears’ physical appearance.

To monitor populations, “we have to be able to recognize individuals,” said Clapham. But bears don’t have any feature comparable to a fingerprint, such as a zebra’s stripes or a giraffe’s spots.

From 4,675 fully labeled bear faces on DSLR photographs, taken from research and bear-viewing sites at Brooks River, Alabama, and Knight Inlet, they randomly split images into training and testing data sets. Once trained from 3,740 bear faces, deep learning went to work “unsupervised,” Clapham said, to see how well it could spot differences between known bears from 935 photographs.

First, the deep learning algorithm finds the bear face using distinctive landmarks like eyes, nose tip, ears and forehead top. Then the app rotates the face to extract, encode and classify facial features.

Also read |&nbsp;<a href="https://www.wionews.com/world/canadas-health-chief-fears-for-peoples-mental-health-as-alcohol-consumption-spikes-304145#:~:text=Canada's%20consumption%20of%20alcohol%20and,bigger%20problem%20of%20mental%20fatigue.">Canada's health chief fears for people's mental health as alcohol consumption spikes</a>

The system identified bears at an accuracy rate of 84%, correctly distinguishing between known bears such as Lucky, Toffee, Flora and Steve.

But how does it actually tell those bears apart? Before the era of deep learning, “we tried to imagine how humans perceive faces and how we distinguish individuals,” said Alexander Loos, a research engineer at the Fraunhofer Institute for Digital Media Technology, in Germany, who was not involved in the study but has collaborated with Clapham in the past. Programmers would manually input face descriptors into a computer.

But with deep learning, programmers input the images into a neural network that figures out how best to identify individuals. “The network itself extracts the features,” Loos said, which is a huge advantage.

He also cautioned that, “It’s basically a black box. You don’t know what it’s doing,” and that if the data set being examined is unintentionally biased, certain errors can emerge.

For instance, if some bears are photographed more often in light than in dark conditions, the lighting difference can cause misclassification of the bears. (Data bias can be a problem in human facial recognition by AI, with misidentifications known to be more likely for people of color).

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Whatever BearID is really doing, Clapham, who recognizes many Knight Inlet bears by sight, was surprised and encouraged by where the program fell short.

“The bears that I confused, the network confused as well,” she said, suggesting that the app behaves similarly to the neural network in her brain.

However, this first release of BearID is just the start. She hopes the open-source application will become more accurate with more inputs, use and time.

The app is of great interest to the Knight Inlet Lodge in Glendale Cove, which has run grizzly bear tours for decades, and its current owner, the Nanwakolas Council, whose members come from First Nations of Canada.

“Fifteen years ago when we started doing land use planning, there was just one provincial bear health expert for the whole province,” said Kikaxklalagee / Dallas Smith, the president of Nanwakolas Council and a member of the Tlowitsis Nation. That hampered the Nations’ understanding of the health of bears on their territory. He said he felt excited that this “Jason Bourne-ish” technology would allow for more informed stewardship of bears. “We’re trying to make it a sustainable, limited footprint operation.”

And BearID may not stop with North American bears, as Clapham is already in conversation with others keen to use it for species like sloth bears, sun bears and Asiatic bears, as well as wolves.

“What we’d love is that one day we have somewhere where people can upload camera trap images and the system tells you not only what species you’ve seen, but also what individual you’ve seen,” and maybe its sex and age as well, she said.

Ed Miller and Mary Nguyen are Silicon Valley software developers by day, but moonlight at solving an unusually fuzzy problem. A few years ago the pair became mesmerized, like many of us, by an Alaskan webcam broadcasting brown bears from Katmai National Park.

Ed Miller and Mary Nguyen are Silicon Valley software developers by day, but moonlight at solving an unusually fuzzy problem. A few years ago the pair became mesmerized, like many of us, by an Alaskan webcam broadcasting brown bears from Katmai National Park. They also happened to be seeking a project to hone their machine learning expertise. “We thought, machine learning is really great at identifying people, what could it do for bears?” Miller said. Could artificial intelligence used for face recognition be harnessed to discern one bear face from another? At Knight Inlet in British Columbia, Canada, Melanie Clapham was pondering the same question. Clapham, a postdoctoral researcher at the University of Victoria working with Chris Darimont of the Raincoast Conservation Foundation, was keen to explore face recognition technology as an aid to her grizzly bear studies. But her expertise was bear biology, not AI. Also read| Reducing greenhouse gas emission even to zero won't stop global warming: Study Fortuitously, the four found a match on Wildlabs.net, an online broker of collaborations between technologists and conservationists. Combining their skill sets, Miller and Nguyen volunteered spare time over several years for this passion project that would eventually bear fruit, reporting the results of their experiment last week in the journal Ecology and Evolution. The project they produced, BearID, could help conservationists monitor the health of bear populations in various parts of the world, and perhaps aid work with other animals, too. They got started by looking for other animals that had gotten the deep learning treatment. “In typical engineering fashion, we’re always looking for a shortcut,” Miller said. They discovered “dog hipsterizer,” a program that found the faces, eyes and noses of dogs in photos and placed rimmed glasses and mustaches on them. “That was where we started,” Nguyen said. Also read| Researchers find method to rapidly clear alcohol from lungs by breathing Although trained on dogs, dog hipsterizer worked reasonably well on the similarly shaped faces of bears, giving them a programming head start. Nevertheless, Nguyen said, the work’s initial stages were tedious. Creating a training data set for the deep learning program involved examining over 4,000 photos with bears in them and then manually highlighting each bear’s eyes, nose and ears by drawing boxes around them so the program could learn to find these features. The system also had to overcome a challenge of brown bears’ physical appearance. To monitor populations, “we have to be able to recognize individuals,” said Clapham. But bears don’t have any feature comparable to a fingerprint, such as a zebra’s stripes or a giraffe’s spots. From 4,675 fully labeled bear faces on DSLR photographs, taken from research and bear-viewing sites at Brooks River, Alabama, and Knight Inlet, they randomly split images into training and testing data sets. Once trained from 3,740 bear faces, deep learning went to work “unsupervised,” Clapham said, to see how well it could spot differences between known bears from 935 photographs. First, the deep learning algorithm finds the bear face using distinctive landmarks like eyes, nose tip, ears and forehead top. Then the app rotates the face to extract, encode and classify facial features. Also read | Canada's health chief fears for people's mental health as alcohol consumption spikes The system identified bears at an accuracy rate of 84%, correctly distinguishing between known bears such as Lucky, Toffee, Flora and Steve. But how does it actually tell those bears apart? Before the era of deep learning, “we tried to imagine how humans perceive faces and how we distinguish individuals,” said Alexander Loos, a research engineer at the Fraunhofer Institute for Digital Media Technology, in Germany, who was not involved in the study but has collaborated with Clapham in the past. Programmers would manually input face descriptors into a computer. But with deep learning, programmers input the images into a neural network that figures out how best to identify individuals. “The network itself extracts the features,” Loos said, which is a huge advantage. He also cautioned that, “It’s basically a black box. You don’t know what it’s doing,” and that if the data set being examined is unintentionally biased, certain errors can emerge. For instance, if some bears are photographed more often in light than in dark conditions, the lighting difference can cause misclassification of the bears. (Data bias can be a problem in human facial recognition by AI, with misidentifications known to be more likely for people of color). Also read| 'Gene within gene': Scientists make worrying discovery about coronavirus nucleotides Whatever BearID is really doing, Clapham, who recognizes many Knight Inlet bears by sight, was surprised and encouraged by where the program fell short. “The bears that I confused, the network confused as well,” she said, suggesting that the app behaves similarly to the neural network in her brain. However, this first release of BearID is just the start. She hopes the open-source application will become more accurate with more inputs, use and time. The app is of great interest to the Knight Inlet Lodge in Glendale Cove, which has run grizzly bear tours for decades, and its current owner, the Nanwakolas Council, whose members come from First Nations of Canada. “Fifteen years ago when we started doing land use planning, there was just one provincial bear health expert for the whole province,” said Kikaxklalagee / Dallas Smith, the president of Nanwakolas Council and a member of the Tlowitsis Nation. That hampered the Nations’ understanding of the health of bears on their territory. He said he felt excited that this “Jason Bourne-ish” technology would allow for more informed stewardship of bears. “We’re trying to make it a sustainable, limited footprint operation.” And BearID may not stop with North American bears, as Clapham is already in conversation with others keen to use it for species like sloth bears, sun bears and Asiatic bears, as well as wolves. “What we’d love is that one day we have somewhere where people can upload camera trap images and the system tells you not only what species you’ve seen, but also what individual you’ve seen,” and maybe its sex and age as well, she said.

Training facial recognition on some new furry friends: Bears

Dubai is all set to introduce the feature of facial recognition system on public transport to assure more security for the locals, the officials announced on Sunday.

"This technology has proven its effectiveness to identify suspicious and wanted people," said Obaid al-Hathboor, director of Dubai's Transport Security Department.

The technology is being brought in a little before the country gears up to host the global Expo exhibition.

Facial recognition has been in user at the Dubai international airport for the last few months as a part of the process of becoming a "smart city" in the Middle East.&nbsp;

Dubai hopes to become one of the leading countries and be a hub for technology and&nbsp;artificial intelligence.

"We aspire to raise our performance by building on our current capabilities, to ensure a high level of security in metro stations and other transport sectors," said Hathboor.

A special police unit has been trained in the United States. The officers&nbsp;conducted a mock attack&nbsp;before working in tandem with a control centre to apprehend the suspects. They also helped the commuters 'evacuate' during this mock attack. These officers will now be deployed at major metro stations during Expo 2020.

This event has been delayed by one year due to the coronavirus pandemic and will now be launching in October 2021. The event was, originally, expected to attract&nbsp;15 million visitors.

Jamal Rashed, of Dubai Police's Transport Security Department, said the facial recognition technology will be rolled out in the coming months in all metro stations.&nbsp;"It took at least five hours to identify a suspect before," said Rashed. "With this technology, it takes less than a minute.

Dubai is all set to introduce the feature of facial recognition system on public transport to assure more security for the locals, the officials announced on Sunday.

Dubai is all set to introduce the feature of facial recognition system on public transport to assure more security for the locals, the officials announced on Sunday. "This technology has proven its effectiveness to identify suspicious and wanted people," said Obaid al-Hathboor, director of Dubai's Transport Security Department. The technology is being brought in a little before the country gears up to host the global Expo exhibition. Facial recognition has been in user at the Dubai international airport for the last few months as a part of the process of becoming a "smart city" in the Middle East. Dubai hopes to become one of the leading countries and be a hub for technology and artificial intelligence. "We aspire to raise our performance by building on our current capabilities, to ensure a high level of security in metro stations and other transport sectors," said Hathboor. A special police unit has been trained in the United States. The officers conducted a mock attack before working in tandem with a control centre to apprehend the suspects. They also helped the commuters 'evacuate' during this mock attack. These officers will now be deployed at major metro stations during Expo 2020. This event has been delayed by one year due to the coronavirus pandemic and will now be launching in October 2021. The event was, originally, expected to attract 15 million visitors. Jamal Rashed, of Dubai Police's Transport Security Department, said the facial recognition technology will be rolled out in the coming months in all metro stations. "It took at least five hours to identify a suspect before," said Rashed. "With this technology, it takes less than a minute.

Dubai's public transport introduces facial recognition for security

Simgapore is set to become the first in the world to use facial verification for national identity cards, which will allow citizens to access private and government services.

The service was earlier tested in a bank, and after its successful trial, it is now being rolled out all over the country.

This technology has been designed by a UK-based company called&nbsp;iProov. As per the initial reports, it has been understood that this technology will help not only recognise the&nbsp;face but also test if the concerned person is actually present there.

"You have to make sure that the person is genuinely present when they authenticate, that you're not looking at a photograph or a video or a replayed recording or a deepfake," said Andrew Bud, founder and chief executive of iProov.

The technology will be combined with Singapore's&nbsp;digital identity scheme SingPass, allowing participants to access government services.

"This is the first time that cloud-based face verification has been used to secure the identity of people who are using a national digital identity scheme," said Bud.

Facial verification technology will also help the police authorities get alerted when a criminal walks past.

"Face recognition has all sorts of social implications. Face verification is extremely benign," Bud said.

While the technology is new and exciting for few, some feel it might give access to the government into locals' private matters too.&nbsp;

However, as of now, the government is not planning to regulate or restrict the technology. "We don't really restrict how this digital face verification can be used, as long as it complies with our requirements," said Kwok Quek Sin, senior director of national digital identity at GovTech Singapore.

"And the basic requirement is that it is done with consent and with the awareness of the individual."

Technology partners and companies think many countries, who have a common national identity card/system, might want to join in on this revolutionary idea soon.

Simgapore is set to become the first in the world to use facial verification for national identity cards, which will allow citizens to access private and government services.

Simgapore is set to become the first in the world to use facial verification for national identity cards, which will allow citizens to access private and government services. The service was earlier tested in a bank, and after its successful trial, it is now being rolled out all over the country. This technology has been designed by a UK-based company called iProov. As per the initial reports, it has been understood that this technology will help not only recognise the face but also test if the concerned person is actually present there. "You have to make sure that the person is genuinely present when they authenticate, that you're not looking at a photograph or a video or a replayed recording or a deepfake," said Andrew Bud, founder and chief executive of iProov. The technology will be combined with Singapore's digital identity scheme SingPass, allowing participants to access government services. "This is the first time that cloud-based face verification has been used to secure the identity of people who are using a national digital identity scheme," said Bud. Facial verification technology will also help the police authorities get alerted when a criminal walks past. "Face recognition has all sorts of social implications. Face verification is extremely benign," Bud said. While the technology is new and exciting for few, some feel it might give access to the government into locals' private matters too. However, as of now, the government is not planning to regulate or restrict the technology. "We don't really restrict how this digital face verification can be used, as long as it complies with our requirements," said Kwok Quek Sin, senior director of national digital identity at GovTech Singapore. "And the basic requirement is that it is done with consent and with the awareness of the individual." Technology partners and companies think many countries, who have a common national identity card/system, might want to join in on this revolutionary idea soon.

Singapore becomes first to use facial verification in national ID

A hospital in India has deployed a customer-service robot to patrol its wards, connecting coronavirus patients to friends and relatives. Mitra, meaning "friend" in Hindi, is best known for interacting with Prime Minister Narendra Modi at an event in 2017.

Mitra the robot helps COVID patients in India speak to loved ones

Computer engineers in Chicago have developed a tool to protect one's online photos from facial recognition.

In an attempt to foil facial recognition&nbsp;technologies, the&nbsp;researchers from the University of Chicago have&nbsp;has developed a tool that disguises photos with pixel-level changes that confuse facial recognition systems.

The tool called&nbsp;Fawkes, named after Guy&nbsp;Fawkes,&nbsp;the software was made available to developers on the researchers' website last month.

It has been downloaded more than 50,000 times after a&nbsp;free app version&nbsp;was introduced for non-coders.

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In the researchers' parlance,&nbsp;Fawkes "cloaks" an image by subtly altering some of the features that facial recognition systems depend on when they construct a person's face print.

The tool and be a broadside if it is deployed&nbsp;against facial recognition systems, poisoning the accuracy of the data sets they gather from the Web.

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Dr Ben Zhao, a professor of computer science at the University of Chicago,&nbsp;said "Our goal is to make Clearview go away."

Many tech companies use&nbsp;scrapes&nbsp;billions of online photos to build tools for police to reveal a person's identity. This includes the infacial&nbsp;recognition&nbsp;technology company Clearview AI.

The New York-based company bills itself as a tool for law enforcement, scraping the internet for publicly available photos and using&nbsp;facial&nbsp;recognition&nbsp;to identify potential suspects and victims.

Critics raised concerns earlier this year about the lack of consent of those searched and the potential for misuse of the service, leading to a joint investigation launched in February into the company by the privacy commissioners of British Columbia, Alberta, Quebec and the federal government.

The company is under investigation in Britain, Canada, and Australia&nbsp;where Clearview said it would no longer offer its&nbsp;facial&nbsp;recognition&nbsp;services.

Computer engineers in Chicago have developed a tool to protect one's online photos from facial recognition. In an attempt to foil facial recognition technologies, the researchers from the University of Chicago have has developed a tool that disguises photos with pixel-level changes that confuse facial recognition systems.

Computer engineers in Chicago have developed a tool to protect one's online photos from facial recognition. In an attempt to foil facial recognition technologies, the researchers from the University of Chicago have has developed a tool that disguises photos with pixel-level changes that confuse facial recognition systems. The tool called Fawkes, named after Guy Fawkes, the software was made available to developers on the researchers' website last month. It has been downloaded more than 50,000 times after a free app version was introduced for non-coders. Also read: Twitter could face a fine of upto $250 million for unauthorised usage of user data In the researchers' parlance, Fawkes "cloaks" an image by subtly altering some of the features that facial recognition systems depend on when they construct a person's face print. The tool and be a broadside if it is deployed against facial recognition systems, poisoning the accuracy of the data sets they gather from the Web. Also read: Spanish pizzeria takes social distancing to another level with new mobile application! Dr Ben Zhao, a professor of computer science at the University of Chicago, said "Our goal is to make Clearview go away." Many tech companies use scrapes billions of online photos to build tools for police to reveal a person's identity. This includes the infacial recognition technology company Clearview AI. The New York-based company bills itself as a tool for law enforcement, scraping the internet for publicly available photos and using facial recognition to identify potential suspects and victims. Critics raised concerns earlier this year about the lack of consent of those searched and the potential for misuse of the service, leading to a joint investigation launched in February into the company by the privacy commissioners of British Columbia, Alberta, Quebec and the federal government. The company is under investigation in Britain, Canada, and Australia where Clearview said it would no longer offer its facial recognition services.

Engineers develop tool called 'Fawkes' to protect online photos from facial recognition