To combat discrimination and ensure the right to privacy, MEPs demand strong safeguards when artificial intelligence tools are used in law enforcement.

In a resolution adopted by 377 in favour, 248 against and 62 abstentions, MEPs point to the risk of algorithmic bias in AI applications and emphasise that human supervision and strong legal powers are needed to prevent discrimination by AI, especially in a law enforcement or border-crossing context. Human operators must always make the final decisions and subjects monitored by AI-powered systems must have access to remedy, say MEPs.

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Rapporteur Petar Vitanov (S&D, BG) said: “Fundamental rights are unconditional. For the first time ever, we are calling for a moratorium on the deployment of facial recognition systems for law enforcement purposes, as the technology has proven to be ineffective and often leads to discriminatory results. We are clearly opposed to predictive policing based on the use of AI as well as any processing of biometric data that leads to mass surveillance. This is a huge win for all European citizens.”

Read the complete article at: https://www.europarl.europa.eu/news/en/press-room/20210930IPR13925/use-of-artificial-intelligence-by-the-police-meps-oppose-mass-surveillance

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https://www.nature.com/articles/d41586-021-00868-5

Kate Crawford

During the pandemic, technology companies have been pitching their emotion-recognition software for monitoring workers and even children remotely. Take, for example, a system named 4 Little Trees. Developed in Hong Kong, the program claims to assess children’s emotions while they do classwork. It maps facial features to assign each pupil’s emotional state into a category such as happiness, sadness, anger, disgust, surprise and fear. It also gauges ‘motivation’ and forecasts grades. Similar tools have been marketed to provide surveillance for remote workers. By one estimate, the emotion-recognition industry will grow to US$37 billion by 2026.

There is deep scientific disagreement about whether AI can detect emotions. A 2019 review found no reliable evidence for it. “Tech companies may well be asking a question that is fundamentally wrong,” the study concluded (L. F. Barrett et al. Psychol. Sci. Public Interest 20, 1–68; 2019).

And there is growing scientific concern about the use and misuse of these technologies. Last year, Rosalind Picard, who co-founded an artificial intelligence (AI) start-up called Affectiva in Boston and heads the Affective Computing Research Group at the Massachusetts Institute of Technology in Cambridge, said she supports regulation. Scholars have called for mandatory, rigorous auditing of all AI technologies used in hiring, along with public disclosure of the findings. In March, a citizen’s panel convened by the Ada Lovelace Institute in London said that an independent, legal body should oversee development and implementation of biometric technologies (see go.nature.com/3cejmtk). Such oversight is essential to defend against systems driven by what I call the phrenological impulse: drawing faulty assumptions about internal states and capabilities from external appearances, with the aim of extracting more about a person than they choose to reveal.

Countries around the world have regulations to enforce scientific rigour in developing medicines that treat the body. Tools that make claims about our minds should be afforded at least the same protection. For years, scholars have called for federal entities to regulate robotics and facial recognition; that should extend to emotion recognition, too. It is time for national regulatory agencies to guard against unproven applications, especially those targeting children and other vulnerable populations.

Read more at: https://www.nature.com/articles/d41586-021-00868-5

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by Karen Hao February 5, 2021

In 1964, mathematician and computer scientist Woodrow Bledsoe first attempted the task of matching suspects’ faces to mugshots. He measured out the distances between different facial features in printed photographs and fed them into a computer program. His rudimentary successes would set off decades of research into teaching machines to recognize human faces.

Now a new study shows just how much this enterprise has eroded our privacy. It hasn’t just fueled an increasingly powerful tool of surveillance. The latest generation of deep-learning-based facial recognition has completely disrupted our norms of consent.

Deborah Raji, a fellow at nonprofit Mozilla, and Genevieve Fried, who advises members of the US Congress on algorithmic accountability, examined over 130 facial-recognition data sets compiled over 43 years. They found that researchers, driven by the exploding data requirements of deep learning, gradually abandoned asking for people’s consent. This has led more and more of people’s personal photos to be incorporated into systems of surveillance without their knowledge.

It has also led to far messier data sets: they may unintentionally include photos of minors, use racist and sexist labels, or have inconsistent quality and lighting. The trend could help explain the growing number of cases in which facial-recognition systems have failed with troubling consequences, such as the false arrests of two Black men in the Detroit area last year.

Read more at: https://www.technologyreview.com/2021/02/05/1017388/ai-deep-learning-facial-recognition-data

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18 NOVEMBER 2020

Journals and researchers are under fire for controversial studies using this technology. And a Nature survey reveals that many researchers in this field think there is a problem.

Richard Van Noorden

In September 2019, four researchers wrote to the publisher Wiley to “respectfully ask” that it immediately retract a scientific paper. The study, published in 2018, had trained algorithms to distinguish faces of Uyghur people, a predominantly Muslim minority ethnic group in China, from those of Korean and Tibetan ethnicity¹.

China had already been internationally condemned for its heavy surveillance and mass detentions of Uyghurs in camps in the northwestern province of Xinjiang — which the government says are re-education centres aimed at quelling a terrorist movement. According to media reports, authorities in Xinjiang have used surveillance cameras equipped with software attuned to Uyghur faces.

As a result, many researchers found it disturbing that academics had tried to build such algorithms — and that a US journal had published a research paper on the topic. And the 2018 study wasn’t the only one: journals from publishers including Springer Nature, Elsevier and the Institute of Electrical and Electronics Engineers (IEEE) had also published peer-reviewed papers that describe using facial recognition to identify Uyghurs and members of other Chinese minority groups. (Nature’s news team is editorially independent from its publisher, Springer Nature.)

The complaint, which launched an ongoing investigation, was one foray in a growing push by some scientists and human-rights activists to get the scientific community to take a firmer stance against unethical facial-recognition research. It’s important to denounce controversial uses of the technology, but that’s not enough, ethicists say. Scientists should also acknowledge the morally dubious foundations of much of the academic work in the field — including studies that have collected enormous data sets of images of people’s faces without consent, many of which helped hone commercial or military surveillance algorithms. (A linked feature explores concerns over algorithmic bias in facial-recognition systems.)

Read more at: https://doi.org/10.1038/d41586-020-03187-3

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Tom Simonite 09.21.2020

The first big US city to prohibit private businesses from using the technology reflects rising skepticism of new tools and concerns about fairness.

PORTLAND’S 2016 ENTRY for a $50 million federal contest called the Smart City Challenge described a Pacific Northwest tech-topia. It promised autonomous shuttles, trucks, and cars on city streets, through partnerships with Daimler and Lyft. Sensors from Alphabet’s Sidewalk Labs would monitor people walking and biking around the city to analyze traffic patterns.

The Rose City didn’t win, and four years later there are no self-driving Lyfts on its streets. One thing that has changed: Portland’s conception of what makes a city smart.

This month, Portland adopted the nation’s most restrictive laws on face recognition, banning private as well as government use of the technology. The new rules originated in part from a small city office called Smart City PDX that has sought to redefine the buzzword it is named for. Instead of hunting for “smart” new tech, it aims to mediate tech’s impact on citizens. “The focus became the work we need to do before we deploy new technology, especially in BIPOC communities who don’t trust the city to necessarily represent their interests,” says Kevin Martin, who leads Smart City PDX.

Read more at: https://www.wired.com/story/portlands-face-recognition-ban-twist-smart-cities/#:~:text=The%20first%20big%20US%20city,tools%20and%20concerns%20about%20fairness.

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For more information see:

https://www.axios.com/5g-deployment-battle-cities-fcc-small-cell-ad1bfba2-2ffd-463f-8ed4-48a3c664249b.html

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Trying to solve poverty, crime, and disease with (often biased) technology doesn’t address their root causes.

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“While AI for good programs often warrant genuine excitement, they should also invite increased scrutiny. Good intentions are not enough when it comes to deploying AI for those in greatest need. In fact, the fanfare around these projects smacks of tech solutionism, which can mask root causes and the risks of experimenting with AI on vulnerable people without appropriate safeguards.”

“Even when a company’s intentions seem coherent, the reality is that for many AI applications, the current state of the art is pretty bad when applied to global populations. Researchers have found that facial recognition software, in particular, is often biased against people of color, especially those who are women. This has led to calls for a global moratorium on facial recognition and cities like San Francisco to effectively ban it. AI systems built on limited training data create inaccurate predictive models that lead to unfair outcomes. AI for good projects often amount to pilot beta testing with unproven technologies. It’s unacceptable to experiment in the real world on vulnerable people, especially without their meaningful consent. And the AI field has yet to figure out who is culpable when these systems fail and people are hurt as a result.”

To read the article: https://www.wired.com/story/opinion-ai-for-good-is-often-bad/

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In 2018, a Russian state-run news agency announced that Russian forces had deployed a remote-controlled robotic tank to Syria.

Despite all this, Haner and Garcia found that there is little oversight or regulation when it comes to developing autonomous weapons, and even less public discourse about it.

“These things are happening now, and it’s really crucial that we start having conversations about what to do about it,” Haner says.

Click on the link below to read the article.

Autonomous weapons systems that utilize artificial intelligence are changing the nature of warfare, but where’s the public discussion?

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Imagine you’re taking an online business class — the kind where you watch video lectures and then answer questions at the end. But this isn’t a normal class, and you’re not just watching the lectures: They’re watching you back. Every time the facial recognition system decides that you look bored, distracted, or tuned out, it makes a note. And after each lecture, it only asks you about content from those moments.

This isn’t a hypothetical system; it’s a real one deployed by a company called Nestor. And if you don’t like the sound of it, you’re not alone. Neither do the actual students.

When I asked the man behind the system, French inventor Marcel Saucet, how the students in these classes feel about being watched, he admitted that they didn’t like it. They felt violated and surveilled, he said, but he shrugged off any implication that it was his fault. “Everybody is doing this,” he told me. “It’s really early and shocking, but we cannot go against natural laws of evolution.”

Read the complete article at https://www.vox.com/the-highlight/2019/10/1/20887003/tech-technology-evolution-natural-inevitable-ethics

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“FOR THE SAFER USE OF TECHNOLOGY”

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