[FRIAM] Fwd: A sneak peek at The Algorithm
Tom Johnson
tom at jtjohnson.com
Fri Feb 12 15:05:58 EST 2021
Perhaps of interest.
T
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Tom Johnson - tom at jtjohnson.com
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From: MIT Technology Review <promotions at technologyreview.com>
Date: Fri, Feb 12, 2021 at 11:35 AM
Subject: A sneak peek at The Algorithm
To: <tom at jtjohnson.com>
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[image: A photo of a face shattering into countless pixels.]
Hello Algorithm readers,
In 1964, mathematician and computer scientist Woodrow Bledsoe
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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
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shows just how much this enterprise has eroded our privacy. It hasn’t just
enabled an increasingly powerful tool of surveillance. The latest
generation of deep learning-based facial recognition has also completely
disrupted our norms of consent.
This week, I’m sharing the piece I wrote about this research, which
includes an interactive version
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of this graphic below. You can hover over the dots, which each represent a
facial recognition dataset to see how many images and people are included
in each one.
[image:
_LNJeYLy4ok-ZMjxHsHasQ6BIUCPNqDxzpjnXT_gZFbFJ-fXx_AO4bDq_2L0nkVFTHRCpcq8sfOF_UrUGdQIPFNdVdabu_k_JlGyoWh4sWkeoz2QhSHtMf9F0B4KqJTj6RPvWacU]
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Deborah Raji, a fellow at nonprofit Mozilla, and Genevieve Fried, who
advises US Congress members on algorithmic accountability, examined over
130 of these data sets compiled across 43 years. They found that
researchers gradually abandoned asking for people’s consent to meet the
exploding data requirements of deep learning. This has led to more and more
of people’s personal photos being unknowingly incorporated into systems of
surveillance.
It has also led to far messier datasets, like those that 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 high-stakes failures of facial recognition systems, such as the false
arrests of two Black men
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in the Detroit area last year.
People were extremely cautious about collecting, documenting, and verifying
face data in the early days, says Raji. “Now we don’t care anymore. All of
that has been abandoned,” she says. “You just can't keep track of a million
faces. After a certain point, you can’t even pretend that you have
control.” *Read the full story here
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*Photo credit: Getty*
Deeper Learning
For more on how facial recognition has affected data privacy, try:
- The full text
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of the research paper
- This tool
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that lets you check if your Flickr photos were used to built facial
recognition, which you can read more about in the New York Times
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- Our coverage of IBM’s 2019 photo-scraping scandal
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based on an NBC investigation
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- Our story about how researchers also train AI on people’s YouTube
videos
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More
*Predictive policing is still racist—whatever data it uses.* It’s no secret
that predictive policing tools are racially biased
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A number of studies have shown that racist feedback loops can arise if
algorithms are trained on police data
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such as arrests. Police are known to arrest more people in Black and other
minority neighborhoods, which leads algorithms to direct more policing to
those areas, which leads to more arrests.
In their defence, many developers of predictive policing tools say that
they have started using victim reports to get a more accurate picture of
crime rates across different neighborhoods. In theory victim reports should
be less biased because they aren’t affected by police prejudice or feedback
loops.
But new research shows that training predictive tools in a way claimed to
lessen bias has little effect. Nil-Jana Akpinar and Alexandra Chouldechova
at Carnegie Mellon University built their own predictive algorithm, trained
on victim report data, using the same model found in several popular tools,
including PredPol, the most widely used system in the US.
When they compared their tool’s predictions against actual crime data for
each district they found that it made significant errors. For example, in a
district where few crimes were reported, the tool only predicted around 20%
of the actual hotspots—locations with a high rate of crime. On the other
hand, in a district with a high number of reports, the tool predicted 20%
more hotspots than there really were. *Read more here
<https://www.cloudhq-mkt9.net/mail_track/link/a75750870fa09981d1_1613160358051?uid=226430&url=https%3A%2F%2Ftechnologyreview.us11.list-manage.com%2Ftrack%2Fclick%3Fu%3D47c1a9cec9749a8f8cbc83e78%26id%3D7eb6ebd5e1%26e%3D4866eb5609>.*
*Photo credit: David Mcnew / Getty*
*Fractals can help AI learn to see more clearly—or at least fairly.*
Researchers
in Japan have shown
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a novel way to teach image-recognition systems: by “pretraining” them first
on computer-generated fractals. Pretraining is a phase in which an AI
learns some basic skills before being trained on more specialized data. A
system for diagnosing medical scans might first learn to identify basic
visual features like shapes and outlines by pretraining on ImageNet, for
example, a database with more than 14 million photos of everyday objects.
Then it will be fine-tuned on a smaller database of medical images until it
recognises subtle signs of disease.
The trouble is, assembling a dataset like ImageNet takes a lot of time and
effort. It could also embed racism and sexism and include images of people
without their consent. Fractal patterns, by contrast, don’t suffer these
issues, and can be found in everything from trees and flowers to clouds and
waves. So the researchers created FractalDB, an endless number of
computer-generated fractals, and used it to pre-trained their algorithm
before fine-tuning it with a set of actual images. They found that it
performed almost as well as models trained on state-of-the-art datasets
like ImageNet. *Read more here
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*If you come across interesting research papers, send them my way to
algorithm at technologyreview.com
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Bits and Bytes
*Clearview AI’s facial recognition app is illegal, says Canada*
Authorities said the company needed citizens’ consent to use their
biometric information, and told the firm to delete facial images from its
database. (NYT
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)
*Amazon is using AI-enabled cameras to watch delivery drivers on the job*
They will record drivers in their vehicles “100% of the time” to flag their
safety infractions. (CNBC
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)
*A chatbot to reincarnate your deceased loved ones*
Microsoft filed a patent for the technology in 2017. It says it doesn’t
have plans to actually build it, but that doesn’t stop others from doing
it. (Washington Post
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)
*+ South Korea has used AI to bring back a dead superstar's voice** (CNN
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*Two Google engineers resign over the forced dismissal of Timnit Gebru*
The fall out from the treatment of the ethical AI co-lead continues. (
Reuters
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)
*+ Our recap of the events (**TR
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*AI needs to be able to understand all the world’s languages*
Mobile technology is not accessible to most of the 700 million illiterate
people around the world. Speech recognition could help fix that. (Scientific
American
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)
*How censorship influences artificial intelligence*
Algorithms learn to associate words with other words. “Democracy” can equal
“stability”—or “chaos.” (WIRED
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)
*+ AI and the list of dirty, obscene, and otherwise bad words (WIRED
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*The adoption of AI in health care comes with uncomfortable trade-offs*
More data means better algorithms and better diagnoses, but also less
privacy and perhaps greater inequality. (VentureBeat
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)
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