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Junghi��Woo is an��IPilogue��Writer and a 3L��������Candidate at��Osgoode��Hall Law School

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We’ve come a long way from the days of legally for their driver’s licenses to store in the provincial facial recognition data bank. Facial recognition technology, despite various controversies, has continued its presence in society. Its biggest client? Law enforcement.

Despite facial recognition technology’s , , and multiple class-action lawsuits in Britain and Australia, the U.S. start-up Clearview AI raised , showing that it is here to stay. Facial recognition technology is only going to get more precise- but at what cost?

How does it work?

While Facial Recognition may have various uses, the is that the software first accesses an internet database of photos and videos, then uses biometrics and algorithms to identify the same people in other photos and videos. Namely, law enforcement can use it to “identify” faces in surveillance software.

Privacy Concerns- Did I consent to this?

Most likely not. ��For example, Clearview AI collected billions of images from people on the internet without any clarification as to whether this was done lawfully or not. This led to Canada’s investigation on the RCMP’s use of Clearview AI, which resulted in the company halting its services in Canada. Four Privacy Commissioners have stated that Clearview AI has violated privacy laws in its methods of collecting data and using personal information for Clearview AI and was the privacy authorities’ recommendations. You are probably thinking back to all those Snapchats of your face, your selfie uploads, and your phone’s facial recognition unlock setting. I am too.

Biases in Facial Recognition Technology

Technology can’t be perfect. One of the main issues to consider is whom technology sets out to serve . Studies have demonstrated The found the algorithm’s performance was at its worst when encountering individuals with darker skin tones

Where does this become a serious problem? Ultimately, law enforcement claims to use this technology to “identify criminals”. What happens when law enforcement relies on this technology, despite its proven inaccuracies, when encountering members of racialized groups? Facial recognition technology is more likely to inaccurately match people’s faces if they were of a certain race. A is the NYPD’s use of facial recognition technology during the Black Lives Matter protests earlier this year.

Robert Julian-Borchak Williams from Michigan was the first person to be wrongfully convicted . Police, relying on surveillance video footage of a shoplifter, handcuffed Mr. Williams on his front lawn in front of his whole family. Mr. Williams was then driven to a detention center, got his mugshot, fingerprints, and DNA taken, and was held overnight, then interrogated the next day. All because of a faulty system that could not differentiate between two Black men.

Trade Secrets Implications

Taking this a step further, we look to another concern with facial recognition technology: the . In the U.S., forensic algorithm vendors (including those who sell facial recognition technology) have relied on to protect their intellectual property, hence, withholding relevant evidence at trial. To properly challenge the reliability of this technology, it is essential to understand how it works. Therefore, relying on intellectual property protections to prevent this does not achieve the true purpose of trade secret law – to incentivize businesses to

What does the Future Hold?

Governments in both the U.S. and Canada have started to realize the risks of using facial recognition technology and consider whether they are truly outweighed by its supposed “benefits”. Representative Mark Takano from California has recently introduced the Justice in Forensic Algorithms Act of 2021, which includes a prohibition on the use of trade secret privileges to withhold relevant evidence in criminal cases.

One may ask, what is facial recognition technology truly useful for? Are any of its uses significant enough to outweigh the serious dangers it poses? Without proper transparency, we cannot assure reliability. If essential information is only in the hands of vendors and law enforcement, what type of harmful power dynamic could this create in society?

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Tiffany Wang is an IPilogue Writer, IP Innovation Clinic Fellow, and a 2L JD Candidate at Osgoode Hall Law School.

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“Always eyes watching you and the voice enveloping you. Asleep or awake, indoors or out of doors, in the bath or bed—no escape. Nothing was your own except the few cubic centimeters in your skull.”

Nineteen Eighty-Four by George Orwell

Sprawling throughout New 91��ɫ City, cameras observe and record faces and movements. These seemingly omnipresent lenses conduct and generate facial-recognition data for the New 91��ɫ Police Department (“NYPD”), capturing footage to identify individuals for criminal enforcement efforts.��

Surveillance by security cameras is not unique to the Big Apple. Chinese police forces deploy , notifying security personnel whenever or target appears in sight. . In total, operate to surveil the population of New 91��ɫ.��

Not only do public cameras tower over the city, but also record information that the NYPD may access with permission.

The Big Apple’s capture action in an area less than two square miles. Over ninety percent of residents are racial minorities.��

, serves the United States federal and state police forces. In 2019, the revealed that Idemia’s algorithms are prone to confuse racial minorities’ faces. Similarly, the

of facial data raises concerns of . If commercial algorithms continue to demonstrate significant errors in identifying individuals with varying skin tones, these concerns will quickly escalate to . Tensions may ultimately result in the NYPD

Could over 15,000 eyes create a dystopia? After all, .����

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This article was previously posted on ��

On June 10, 2021, the Office of the Privacy Commissioner of Canada (OPC) issued a����following its investigation into the RCMP’s use of Clearview AI’s facial recognition technology (FRT), reporting that the RCMP contravened the��Privacy Act��(the Act) when it collected information from Clearview AI.

Clearview AI scraped more than three billion images from internet websites without users’ consent to create a databank for clients, such as the RCMP, to use for the purpose of identifying individuals by matching photographs to images in the databank. In February 2021, the OPC along with multiple provincial counterparts, found that Clearview AI’s methods constituted mass surveillance and were illegal under federal and provincial private sector privacy laws, as previously reported by the E-TIPS® Newsletter��. Following this finding, the OPC began its investigation into whether the RCMP contravened the Act when it used Clearview AI’s services.

In a����to Parliament, the OPC shared its findings, where it stated that a government institution cannot collect personal information from a third party if the third party’s collection was unlawful. Given that Clearview AI’s personal information collection practices were found to be illegal, the RCMP’s subsequent collection of that information falls outside its legitimate operating programs and activities and contravenes Section 4 of the Act. The OPC further concluded that the RCMP had an onus to ensure that the databank was compiled according to legitimate privacy practices. Although the RCMP argued that it did not contravene the Act and the onus is unreasonable, it ultimately agreed to follow the OPC’s recommendations and implement changes to its policies, systems, and training. This includes conducting complete privacy assessments of third-party data collection policies to confirm compliance with privacy legislation and ensuring new technologies are on-boarded in a manner that respects privacy rights.

With the rise in police use of FRT, the OPC saw this matter as an opportunity to address serious privacy concerns in the space. Accordingly, the OPC and its provincial and territorial counterparts have launched a consultation on draft����to clarify the privacy obligations for police agencies’ use of FRT. The draft guidance highlights the need for law enforcement officials to have lawful authority for the proposed use of the technology and the importance of implementing privacy standards that are proportionate to the potential harms involved. Comments on the draft guidance may be submitted until October 15, 2021 and further information on how to contribute can be found��.

The post Privacy Commissioner Of Canada Releases Statement On RCMP’s Use Of Clearview AI And Draft Guidance On Use Of Facial Recognition Technology appeared first on IPOsgoode.

In 2019,����started licencing facial recognition software to law enforcement agencies in Canada and the United States. In addition to��, the��. At first, the product seemed promising. Clearview AI’s application helped law enforcement officers track down otherwise unidentifiable criminals. However, Privacy Commissioners for , , and immediately launched a joint investigation into the company when news reports began to circulate raising questions and concerns about Clearview AI’s facial recognition technology.

The problem with Clearview AI’s application is that it requires a large biometric dataset in order to operate. To obtain biometric information, Clearview AI has scraped the Internet and collected over 3.3 billion images of faces and associated data from publicly accessible online sources, including Facebook, YouTube and Instagram. Clearview AI uses facial recognition software to create a biometric array for each of its images. When a user uploads a photograph, Clearview AI assesses its biometric data and retrieves images with corresponding information from its database. Each image in its database contains metadata and a link to its original source, so users can cross-reference and identify people using images found online.

Now, almost a year since news first broke about Clearview AI, Canada’s Privacy Commissioners have finally concluded their investigation into the company. In a����issued on February 2, 2021, the Commissioners collectively condemned Clearview AI for collecting, using and disclosing personal information without the requisite consent. Subject to Canadian privacy laws governing private sector entities, Clearview AI’s activities contravene principle 4.3 of Schedule 1, as well as section 6.1 of , section 7(1) of , sections 6-8 of , and sections 6 and 12-14 of .��

. However, many are now calling on the Trudeau government to ban federal law enforcement and intelligence agencies from using facial recognition for surveillance purposes entirely. Last July, individuals and organizations representing privacy, human rights and civil liberty advocates penned an��, calling on the federal government to “[e]stablish clear and transparent policies and laws regulating the use of facial recognition in Canada, including reforms to the Personal Information Protection and Electronic Documents Act (PIPEDA) and the Privacy Act.”��

Law enforcement agencies can now identify suspects within a matter of seconds. With facial recognition, all it takes is a single photograph to obtain a wealth of personal information about an individual. However, everything comes at a cost, and utilizing facial recognition for law enforcement purposes is no exception. If I have learned anything from Michel Foucault, it is that collective security should not come at the expense of individual autonomy. Do you disagree?

Lamont Abramczyk is a JD Candidate at Osgoode Hall Law School. He is the Deputy Director of the Osgoode Art Law Society and an IP Osgoode Innovation Clinic Fellow.

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Facial recognition technology works by creating a of a person’s face by measuring precise characteristics, such as the distance between the eyes. The template is then compared against other templates already stored in the database to find a possible match. According to, putting on a mask blocks access to the part of our biometrics that most uniquely sets us apart from others – the central portion of the face, just above the brow line down to the chin.

Still, this relief for privacy advocates may only be momentary, as companies are coming up with solutions to identify individuals even with masks. For example, researchers published to help artificial intelligence adapt. The dataset was created by scouring Instagram using the tag “mask” for selfies shared publicly. The creators behind the dataset assert that since the selfies were public, they did not need to to use those photos.

This is not the first time a facial recognition company has asserted that building a database using publicly available photos does not violate privacy rights. , I wrote about the privacy implications of Toronto Police using Clearview AI – a facial recognition technology application that has scraped over 3 billion images of people from social media websites such as Facebook, Twitter, and Venmo. By uploading a photo of a person into the database, you can find other identifying information such as their name, address, and occupation.

As a result of a of , Clearview AI is facing a class-action lawsuit. Residents in Illinois allege the company violated the legislation that requires entities to obtain consent before collecting and storing personal information. BIPA has been somewhat successful in protecting consumer’s facial and biometric data. In January of this year, Facebook agreed to pay to settle a class-action lawsuit over its use of facial recognition technology. Claimants in Illinois alleged that Facebook collected facial recognition data on images for its feature without providing adequate disclosure.

In response to the class action filed in Illinois, and perhaps because of Facebook’s settlement, Clearview AI asserted termination of its relations with non-law enforcement entities in a . However, at the heart of the discussion around Clearview AI, and other facial recognition technology, is the idea that images that are publicly available are up for grabs.

When a person makes their picture public, do they retain some expectation of privacy? Or, do they forego all expectations of privacy? Should we take into consideration the intention behind allowing the public to view a picture? suggest that while people may not expect complete privacy, they do have certain expectations as to how their images may be used. They may not consent to their picture aiding in the development of a database used by local law enforcement or government surveillance in countries known for committing .

Realistically, in today’s society, it isn’t easy to make every picture of your face private. Even though you can make your account private, having your profile picture visible to the general public makes it easier to identify friends or acquaintances. Building a brand, either of an individual or a company, necessitates strategically sharing information using social media.

From a legal perspective, the Canadian courts have not yet had to answer whether an individual foregoes every right to privacy once they make a picture publicly available. In a , it was determined that an individual does not have a reasonable expectation of privacy to information they submit when applying for a driver’s licence. In that specific case, the police had used facial recognition technology on the Ministry of Transportation of Ontario database to find an individual they believed to be the owner of fraudulent licenses.

Last year, the Supreme Court of Canada clarified in the case of that privacy is “not an all or nothing” right. Simply because an individual is in public does not automatically negate all expectations of privacy. It is possible that, in the future, a similar perspective will guide the analysis of online pictures.

So, what does this mean for the everyday person running their errands in a mask? Do they need to worry about the implications of these companies collecting images if the results are inaccurate? The answer is likely, yes. The lack of accuracy of facial recognition technology is , yet different sectors of society rely on the tool. The testing of algorithms for people wearing masks is , and companies are not likely to stop trying to adapt. So, for the time being, you may want to keep your mask selfie on private or not share it at all.

Nikita Munjal is an IPilogue Editor, Clinic Fellow with the Innovation Clinic, and a JD/MBA Candidate at Osgoode Hall Law School.

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