#Facial Recognition Systems

[tektronixtechnology]

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Tektronix Technology presents facial recognition software in Dubai Abu Dhabi

[newyorkweekly]

Ideal Innovations, Inc. Breaks Barriers in Facial Identification Discipline

This is where Ideal Innovations, Inc (I-3) comes in.  I-3 has decades of experience, a strong foundation of expertise and enduring relationships with government agencies—which allow this firm to maintain an unparalleled position of leadership in the Facial Identification industry. The key to I-3’s success lies in its ability to train local and state law enforcement agencies, the military, and corporate security divisions in how to analyze data generated by Facial Recognition systems. I-3 is also working with officials at all levels of government to codify protocols, guidelines, and best practices in local, state and federal law; and to establish a certification process. 

[i4mth4ti4m]

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O Jesus, through the Immaculate Heart of Mary,

I offer you my prayers, works, joys and sufferings of this day

for all the intentions of your Sacred Heart,

in union with the Holy Sacrifice of the Mass throughout the world,

for the salvation of souls, the reparation for sins, the reunion of all Christians,

and in particular for the intentions of the Holy Father this month.

Amen

⋆⁺₊⋆ ☀︎ ⋆⁺₊⋆ ⋆⁺₊⋆ ☀︎ ⋆⁺₊⋆ ⋆⁺₊⋆ ☀︎ ⋆⁺₊⋆ ⋆⁺₊⋆

[news4dzhozhar]

[ausetkmt]

The first time Karl Ricanek was stopped by police for “driving while Black” was in the summer of 1995. He was twenty-five and had just qualified as an engineer and started work at the US Department of Defense’s Naval Undersea Warfare Center in Newport, Rhode Island, a wealthy town known for its spectacular cliff walks and millionaires’ mansions. That summer, he had bought his first nice car—a two-year-old dark green Infiniti J30T that cost him roughly $30,000 (US).

One evening, on his way back to the place he rented in First Beach, a police car pulled him over. Karl was polite, distant, knowing not to seem combative or aggressive. He knew, too, to keep his hands in visible places and what could happen if he didn’t. It was something he’d been trained to do from a young age.

The cop asked Karl his name, which he told him, even though he didn’t have to. He was well aware that if he wanted to get out of this thing, he had to cooperate. He felt at that moment he had been stripped of any rights, but he knew this was what he—and thousands of others like him—had to live with. This is a nice car, the cop told Karl. How do you afford a fancy car like this?

What do you mean? Karl thought furiously. None of your business how I afford this car. Instead, he said, “Well, I’m an engineer. I work over at the research centre. I bought the car with my wages.”

That wasn’t the last time Karl was pulled over by a cop. In fact, it wasn’t even the last time in Newport. And when friends and colleagues shrugged, telling him that getting stopped and being asked some questions didn’t sound like a big deal, he let it lie. But they had never been stopped simply for “driving while white”; they hadn’t been subjected to the humiliation of being questioned as law-abiding adults, purely based on their visual identity; they didn’t have to justify their presence and their choices to strangers and be afraid for their lives if they resisted.

Karl had never broken the law. He’d worked as hard as anybody else, doing all the things that bright young people were supposed to do in America. So why, he thought, can’t I just be left alone?

Karl grew up with four older siblings in Deanwood, a primarily Black neighbourhood in the northeastern corner of Washington, DC, with a white German father and a Black mother. When he left Washington, DC, at eighteen for college, he had a scholarship to study at North Carolina A&T State University, which graduates the largest numbers of Black engineers in the US. It was where Karl learned to address problems with technical solutions, rather than social ones. He taught himself to emphasize his academic credentials and underplay his background so he would be taken more seriously amongst peers.

After working in Newport, Karl went into academia, at the University of North Carolina, Wilmington. In particular, he was interested in teaching computers to identify faces even better than humans do. His goal seemed simple: first, unpick how humans see faces, and then teach computers how to do it more efficiently.

When he started out back in the ’80s and ’90s, Karl was developing AI technology to help the US Navy’s submarine fleet navigate autonomously. At the time, computer vision was a slow-moving field, in which machines were merely taught to recognize objects rather than people’s identities. The technology was nascent—and pretty terrible. The algorithms he designed were trying to get the machine to say: that’s a bottle, these are glasses, this is a table, these are humans. Each year, they made incremental, single-digit improvements in precision.

Then, a new type of AI known as deep learning emerged—the same discipline that allowed miscreants to generate sexually deviant deepfakes of Helen Mort and Noelle Martin, and the model that underpins ChatGPT. The cutting-edge technology was helped along by an embarrassment of data riches—in this case, millions of photos uploaded to the web that could be used to train new image recognition algorithms.

Deep learning catapulted the small gains Karl was seeing into real progress. All of a sudden, what used to be a 1 percent improvement was now 10 percent each year. It meant software could now be used not just to classify objects but to recognize unique faces.

When Karl first started working on the problem of facial recognition, it wasn’t supposed to be used live on protesters or pedestrians or ordinary people. It was supposed to be a photo analysis tool. From its inception in the ’90s, researchers knew there were biases and inaccuracies in how the algorithms worked. But they hadn’t quite figured out why.

The biometrics community viewed the problems as academic—an interesting computer-vision challenge affecting a prototype still in its infancy. They broadly agreed that the technology wasn’t ready for prime-time use, and they had no plans to profit from it.

As the technology steadily improved, Karl began to develop experimental AI analytics models to spot physical signs of illnesses like cardiovascular disease, Alzheimer’s, or Parkinson’s from a person’s face. For instance, a common symptom of Parkinson’s is frozen or stiff facial expressions, brought on by changes in the face’s muscles. AI technology could be used to analyse these micro muscular changes and detect the onset of disease early. He told me he imagined inventing a mirror that you could look at each morning that would tell you (or notify a trusted person) if you were developing symptoms of degenerative neurological disease. He founded a for-profit company, Lapetus Solutions, which predicted life expectancy through facial analytics, for the insurance market.

His systems were used by law enforcement to identify trafficked children and notorious criminal gangsters such as Whitey Bulger. He even looked into identifying faces of those who had changed genders, by testing his systems on videos of transsexual people undergoing hormonal transitions, an extremely controversial use of the technology. He became fixated on the mysteries locked up in the human face, regardless of any harms or negative consequences.

In the US, it was 9/11 that, quite literally overnight, ramped up the administration’s urgent need for surveillance technologies like face recognition, supercharging investment in and development of these systems. The issue was no longer merely academic, and within a few years, the US government had built vast databases containing the faces and other biometric data of millions of Iraqis, Afghans, and US tourists from around the world. They invested heavily in commercializing biometric research like Karl’s; he received military funding to improve facial recognition algorithms, working on systems to recognize obscured and masked faces, young faces, and faces as they aged. American domestic law enforcement adapted counterterrorism technology, including facial recognition, to police street crime, gang violence, and even civil rights protests.

It became harder for Karl to ignore what AI facial analytics was now being developed for. Yet, during those years, he resisted critique of the social impacts of the powerful technology he was helping create. He rarely sat on ethics or standards boards at his university, because he thought they were bureaucratic and time consuming. He described critics of facial recognition as “social justice warriors” who didn’t have practical experience of building this technology themselves. As far as he was concerned, he was creating tools to help save children and find terrorists, and everything else was just noise.

But it wasn’t that straightforward. Technology companies, both large and small, had access to far more face data and had a commercial imperative to push forward facial recognition. Corporate giants such as Meta and Chinese-owned TikTok, and start-ups like New York–based Clearview AI and Russia’s NTech Labs, own even larger databases of faces than many governments do—and certainly more than researchers like Karl do. And they’re all driven by the same incentive: making money.

These private actors soon uprooted systems from academic institutions like Karl’s and started selling immature facial recognition solutions to law enforcement, intelligence agencies, governments, and private entities around the world. In January 2020, the New York Times published a story about how Clearview AI had taken billions of photos from the web, including sites like LinkedIn and Instagram, to build powerful facial recognition capabilities bought by several police forces around the world.

The technology was being unleashed from Argentina to Alabama with a life of its own, blowing wild like gleeful dandelion seeds taking root at will. In Uganda, Hong Kong, and India, it has been used to stifle political opposition and civil protest. In the US, it was used to track Black Lives Matter protests and Capitol rioters during the uprising in January 2021, and in London to monitor revellers at the annual Afro-Caribbean carnival in Notting Hill.

And it’s not just a law enforcement tool: facial recognition is being used to catch pickpockets and petty thieves. It is deployed at the famous Gordon’s Wine Bar in London, scanning for known troublemakers. It’s even been used to identify dead Russian soldiers in Ukraine. The question whether it was ready for prime-time use has taken on an urgency as it impacts the lives of billions around the world.

Karl knew the technology was not ready for widespread rollout in this way. Indeed, in 2018, Joy Buolamwini, Timnit Gebru, and Deborah Raji—three Black female researchers at Microsoft—had published a study, alongside collaborators, comparing the accuracy of face recognition systems built by IBM, Face++, and Microsoft. They found the error rates for light-skinned men hovered at less than 1 percent, while that figure touched 35 percent for darker-skinned women. Karl knew that New Jersey resident Nijer Parks spent ten days in jail in 2019 and paid several thousand dollars to defend himself against accusations of shoplifting and assault of a police officer in Woodbridge, New Jersey.

The thirty-three-year-old Black man had been misidentified by a facial recognition system used by the Woodbridge police. The case was dismissed a year later for lack of evidence, and Parks later sued the police for violation of his civil rights.

A year after that, Robert Julian-Borchak Williams, a Detroit resident and father of two, was arrested for a shoplifting crime he did not commit, due to another faulty facial recognition match. The arrest took place in his front garden, in front of his family.

Facial recognition technology also led to the incorrect identification of American-born Amara Majeed as a terrorist involved in Sri Lanka’s Easter Day bombings in 2019. Majeed, a college student at the time, said the misidentification caused her and her family humiliation and pain after her relatives in Sri Lanka saw her face, unexpectedly, amongst a line-up of the accused terrorists on the evening news.

As his worlds started to collide, Karl was forced to reckon with the implications of AI-enabled surveillance—and to question his own role in it, acknowledging it could curtail the freedoms of individuals and communities going about their normal lives. “I think I used to believe that I create technology,” he told me, “and other smart people deal with policy issues. Now I have to ponder and think much deeper about what it is that I’m doing.”

And what he had thought of as technical glitches, such as algorithms working much better on Caucasian and male faces while struggling to correctly identify darker skin tones and female faces, he came to see as much more than that.

“It’s a complicated feeling. As an engineer, as a scientist, I want to build technology to do good,” he told me. “But as a human being and as a Black man, I know people are going to use technology inappropriately. I know my technology might be used against me in some manner or fashion.”

In my decade of covering the technology industry, Karl was one of the only computer scientists to ever express their moral doubts out loud to me. Through him, I glimpsed the fraught relationship that engineers can have with their own creations and the ethical ambiguities they grapple with when their personal and professional instincts collide.

He was also one of the few technologists who comprehended the implicit threats of facial recognition, particularly in policing, in a visceral way.

“The problem that we have is not the algorithms but the humans,” he insisted. When you hear about facial recognition in law enforcement going terribly wrong, it’s because of human errors, he said, referring to the over-policing of African American males and other minorities and the use of unprovoked violence by police officers against Black people like Philando Castile, George Floyd, and Breonna Taylor.

He knew the technology was rife with false positives and that humans suffered from confirmation bias. So if a police officer believed someone to be guilty of a crime and the AI system confirmed it, they were likely to target innocents. “And if that person is Black, who cares?” he said.

He admitted to worrying that the inevitable false matches would result in unnecessary gun violence. He was afraid that these problems would compound the social malaise of racial or other types of profiling. Together, humans and AI could end up creating a policing system far more malignant than the one citizens have today.

“It’s the same problem that came out of the Jim Crow era of the ’60s; it was supposed to be separate but equal, which it never was; it was just separate . . . fundamentally, people don’t treat everybody the same. People make laws, and people use algorithms. At the end of the day, the computer doesn’t care.”

Excerpted from Code Dependent: Living in the Shadow of AI by Madhumita Murgia. Published by Henry Holt and Company. Copyright © 2024 by Madhumita Murgia. All rights reserved.

[awesomecooperlove]

👹👹👹

[chewwytwee]

If we want to have productive conversations about ‘AI’ we need to start thinking about what we’re actually talking about.

AI is a catch-all term that can apply to almost any situation where a computer is allowed to make decisions. Mid journey is a generative image platform, character ai is a large language model, facial recognition is not either of those things, machine learning is neither of those things.

‘AI’ isn’t something you can fight because you’d have to argue for shit like un-automating light houses and removing all enemy programming in video games because those are also ‘Artificial intelligence’. This all isn’t to say that AI is amazing and great, but it is to say that ‘AI’ is almost a totally useless term when you’re trying to talk about specific technologies that contribute to systemic inequality.

[perfectiongeeks]

Facial Recognition System Development: The Why’s and How’s

Facial recognition technology has seen significant advancements and increased adoption over the last decade. From unlocking smartphones to sophisticated security systems, facial recognition is transforming the way we interact with technology. As businesses and organizations look to integrate this technology, understanding the why's and how's of facial recognition system development is crucial. This comprehensive guide delves into the reasons behind the growing interest in facial recognition systems and provides an in-depth look at the development process.

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[jcmarchi]

A mindset for fairer AI in criminal justice

New Post has been published on https://thedigitalinsider.com/a-mindset-for-fairer-ai-in-criminal-justice/

A mindset for fairer AI in criminal justice

Artificial intelligence (AI) is being discussed nearly everywhere these days, including in legal circles. AI promises efficiency and objectivity, which are sorely needed in the justice system, but there are also horror stories, including racial bias against criminal defendants and even innocent individuals being wrongfully arrested.

The root cause often lies in the inherent biases within some algorithms that power AI systems, but the problem runs deeper than that. It’s also about the data the systems are trained on, the goals we set for AI systems, how those are applied, and how we interpret the results. It’s not just technology – it’s us.

Enter Public Interest Technology (PIT), which we can think of as an essential mindset that focuses us on selecting, implementing, and evaluating AI systems in ways that are fair, just, and human-centered. It’s an approach that sets our sights squarely on the decisions that are most important when it comes to protecting people from the actual harms of bias and discrimination.

Public Interest Technology can act as a guiding framework that supports the development, implementation, and governance of AI in the criminal justice system to ensure fairness, transparency, and accountability.

What exactly is Public Interest Technology?

Public Interest Technology is a human-centered approach to technology that prioritizes social justice, fairness, and equity in the design, development, and implementation of technological solutions.

Darren Walker, president of the Ford Foundation, explains that PIT focuses less on the technology itself and more on ethics, human rights, and social justice [1]. It emphasizes a socio-technological approach that prioritizes people’s needs over unchecked technological development. In essence, PIT seeks to ensure that technology serves us and not the other way around.

This means designing, using, and regulating technology to benefit everyone, especially those from vulnerable or historically marginalized groups. It’s about making sure everyone has a say in decisions about the tech that affects their lives.

AI in justice contexts

AI is already used in the criminal justice system to identify suspects, predict re-offense risk, and suggest criminal sentences. These are all powerful tools that promise to improve justice outcomes and positively affect society as a whole. 

However, these same tools can and have perpetuated discrimination when not carefully and thoughtfully applied. 

According to the ACLU, “…there have been at least seven wrongful arrests we know of in the United States due to police reliance on incorrect face recognition results — and those are just the known cases. In nearly every one of those instances, the person wrongfully arrested was Black” [2].

Further, recidivism prediction tools, such as COMPAS, have been criticized as unfairly categorizing Black men as high-risk for reoffense when compared to their White counterparts [3].  Some criminal courts are using this information to inform the sentencing decisions judges make [4]. Even worse, these AI tools are often opaque, meaning the decision-making processes they use are either unclear or entirely unknown.

Tackling algorithmic bias head-on

Algorithmic bias in facial recognition and recidivism prediction tools occurs in part due to biased data, poorly devised algorithms, and problematic feature sets.  But it’s also due to a lack of human guidance and governance structures that restrain, shape, and guide the safe implementation of the technology. PIT not only emphasizes improving the technology itself but also stresses continued human management of those systems to recognize, address, and eliminate biased outcomes altogether.

For instance, researchers in New Zealand are developing transparent models for assessing assault cases in criminal courts [5]. Unlike the COMPAS program described above, these researchers are developing transparent AI models that open the model’s decisions to scrutiny. By making the inner workings of the AI clear, it’s easier to identify and correct potential biases and thereby prevent harm. 

This aligns with the core PIT principles of transparency and accountability that contribute to fair outcomes and societal trust in these systems.

Human in the Loop

In addition to improving transparency, PIT also highlights the importance of human oversight. The concept of having a human in the loop is mandatory to ensure fairness, accountability, and transparency [6]. AI can be powerful in many respects, but it cannot replace human judgment, especially in high-stakes settings like the justice system. 

Humans should not only be involved in developing and using AI, but they should always have the power to override AI-based decisions in any given case. This doesn’t guarantee fairer outcomes (human judges can be biased, too), but it does create accountability for the final result. It’s impossible to hold an algorithm accountable. It’s entirely possible to criticize and potentially remove an unfair judge.

A fairer tech future

PIT isn’t a magic solution. Mindsets alone will not solve the problems that AI poses to society. However, it does focus our attention on implementing AI systems in ways that promote justice and equity, especially in the most sensitive of areas, like the criminal justice system. 

By upholding values like fairness, transparency, and human oversight, PIT can help us minimize AI risks and ensure that this powerful technology serves society as a whole.

As AI becomes further intertwined with our lives, PIT will become even more crucial. By working together – technologists, policymakers, advocates, and the public – we can build a future where AI is a force for good, not harm. 

After all, technology should always be a tool for justice, not a weapon of discrimination.

References

[1] Walker, D. (n.d.). Deprogramming Implicit Bias: The Case for Public Interest Technology. https://doi.org/10.1162/daed_a_02059

[2] Wessler, N. F. (2024, April 30). Police Say a Simple Warning Will Prevent Face Recognition Wrongful Arrests. That’s Just Not True. | ACLU. American Civil Liberties Union. https://www.aclu.org/news/privacy-technology/police-say-a-simple-warning-will-prevent-face-recognition-wrongful-arrests-thats-just-not-true#:~:text=To%20date%2C%20there%20have%20ben

[3] Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine Bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

[4] Hao, K. (2019, January 21). AI Is Sending People to Jail—and Getting It Wrong. MIT Technology Review. https://www.technologyreview.com/2019/01/21/137783/algorithms-criminal-justice-ai/

[5] Rodger, H., Lensen, A., & Betkier, M. (2022). Explainable artificial intelligence for assault sentence prediction in New Zealand. Journal of the Royal Society of New Zealand, 53(1), 133–147. https://doi.org/10.1080/03036758.2022.2114506

[6] Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2022). Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56. https://doi.org/10.1007/s10462-022-10246-w

Interested to know more about bias and the human mind?

Make sure to give the article below a read:

A snapshot of bias, the human mind, and AI

Understanding human bias, AI systems, and leadership challenges in technology management and their impacts on decision-making.

[chambersevidence]

Life support and defense systems monitoring every possible aspect of every individual constantly at all times to ensure safety and protection.

[tektronixtechnology]

[crypticdesire]

sometimes life feels like the truman show, especially when it comes to wearing masks. like most places, the majority of the people in my city don’t wear masks, and i encounter so many people who become uncomfortable or even irritated/hostile when they see someone who is.

i was volunteering and met the coordinator for the first time in person and she was like “look at you being good and wearing your mask. i really need to get better about it” 🧍we both know those are empty words bc if she really cared about the importance of wearing a mask, she would be wearing one….

[third-eyeai]

[expediteiot]

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[astiinfotech1]

Facial recognition systems need a database or a pre-recorded data set to compare captured images and identify faces. A complete high-end configuration unit is installed in the institute and the data capturing process is initiated. The camera mounted with the machine captures and processes the images of students with various angles and qualities along with the basic identification details for further processing.The Image is processed in this way to take care of image quality & other factors.