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Why then attack on the Capitol was inevitable

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Why then attack on the Capitol was inevitable


Maybe you saw this coming nearly a decade ago, when #YourSlipIsShowing laid bare how racist Twitter users were impersonating Black women on the internet. Maybe, for you, it was during Gamergate, the online abuse campaign targeting women in the industry. Or maybe it was the mass shooting in Christchurch, when a gunman steeped in the culture of 8chan livestreamed himself murdering dozens of people. 

Maybe it was when you, or your friend, or your community, became the target of an extremist online mob, and you saw online anger become real world danger and harm. 

Or maybe what happened on Wednesday, when a rabble of internet-fuelled Trump supporters invaded the Capitol, came as a surprise.

For weeks they had been planning their action in plain sight on the internet—but they have been showing you who they are for years. The level of shock you feel right now about the power and danger of online extremism depends on whether you were paying attention. 

The consequences of inaction

The mob who tried to block Congress from confirming Joe Biden’s presidential victory  showed how the stupidity and danger of the far-right internet could come into the real world again, but this time it struck at the center of the US government. Neo-nazi streamers weren’t just inside the Capitol, they were putting on a show for audiences of tens of thousands of people who egged them on in the chats. The mob was having fun doing memes in the halls of American democracy as a woman—a Trump supporter whose social media history shows her devotion to QAnon—was killed trying to break into Congressional offices.

The past year, especially since the pandemic, has been one giant demonstration of the consequences of inaction; the consequences of ignoring the many, many people who have been begging social media companies to take the meme-making extremists and conspiracy theorists that have thrived on their platforms seriously. 

Facebook and Twitter acted to slow the rise of QAnon over the summer, but only after the pro-Trump conspiracy theory was able to grow relatively unrestricted there for three years. Account bans and algorithm tweaks have long been too little, too late to deal with racists, extremists and conspiracy theorists, and they have rarely addressed the fact that these powerful systems were working exactly as intended.    

I spoke with a small handful of the people who could have told you this was coming about this for a story in October. Researchers, technologists, and activists told me that major social media companies have, for the entirety of their history, chosen to do nothing, or to act only after their platforms cause abuse and harm. 

Ariel Waldman tried to get Twitter to meaningfully address abuse there in 2008. Researchers like Shafiqah Hudson, I’Nasah Crockett, and Shireen Mitchell have tracked exactly how harassment works and finds an audience on these platforms for years. Whitney Phillips talked about how she’s haunted by laughter—not just from other people, but also her own—back in the earliest days of her research into online culture and trolling, when overwhelmingly white researchers and personalities treated the extremists among them as edgy curiosities.

Many, many people who have been begging social media companies to take the meme-making extremists and conspiracy theorists seriously.

Ellen Pao, who briefly served as CEO of Reddit in 2014 and stepped down after introducing the platform’s first anti-harassment policy, was astonished that Reddit had only banned r/The_Donald in June 2020, after evidence had built for years to show that the popular pro-Trump message board served as an organizing space for extremiss and a channel for mob abuse. Of course, by the time it was banned, many of its users had already migrated away from Reddit to TheDonald.win, an independent forum created by the same people who ran the previous version. Its pages were filled with dozens of calls for violence ahead of Wednesday’s rally-turned-attempted-coup. 

Banning Trump doesn’t solve the issue

Facebook, Twitter, and YouTube didn’t create conspiracy thinking, or extremist ideologies, of course. Nor did they invent the idea of dangerous personality cults. But these platforms have—by design—handed those groups the mechanisms to reach much larger audiences much faster, and to recruit and radicalize new converts, even at the expense of the people and communities those ideologies target for abuse. And crucially, even when it was clear what was happening, they chose the minimal amount of change—or decided not to intervene at all. 

In the wake of the attempted coup on the Capitol building, people are again looking at the major social media companies to see how they respond. The focus is on Trump’s personal accounts, which he used to encourage supporters to descend on DC and then praised them when they did. Will he be banned from Twitter? There are compelling arguments for why he should. 

But as heavy and consequential as that would be, it’s also, in other ways… not. Abuse, harassment, conspiracy thinking, and racism will still be able to benefit from social media companies that remain interested in only acting when it’s too late, even without Trump retweeting them and egging them on. 

Facebook has banned Trump indefinitely, and also increased the extent of their moderation of groups, where a lot of conspiracy-fueled activity lives. These changes are good, but again, not new: people have told Facebook about this for years; Facebook employees have told Facebook about this for years. Groups were instrumental in organizing Stop the Steal protests in the days after the election, and before that, in anti-mask protests, and before that in spreading fake news, and before that in as a central space for anti-vaccine misinformation. None of this is new. 

There are only so many ways to say that more people should have listened. If you’re paying attention now, maybe you’ll finally start hearing what they say. 

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NASA inches closer to printing artificial organs in space

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3D printed tissue


In America, at least 17 people a day die waiting for an organ transplant. But instead of waiting for a donor to die, what if we could someday grow our own organs?

Last week, six years after NASA announced its Vascular Tissue Challenge, a competition designed to accelerate research that could someday lead to artificial organs, the agency named two winning teams. The challenge required teams to create thick, vascularized human organ tissue that could survive for 30 days. 

The two teams, named Winston and WFIRM, both from the Wake Forest Institute for Regenerative Medicine, used different 3D-printing techniques to create lab-grown liver tissue that would satisfy all of NASA’s requirements and maintain their function.

“We did take two different approaches because when you look at tissues and vascularity, you look at the body doing two main things,” says Anthony Atala, team leader for WFIRM and director of the institute. 

The two approaches differ in the way vascularization—how blood vessels form inside the body—is achieved. One used tubular structures and the other spongy tissue structures to help deliver cell nutrients and remove waste. According to Atala, the challenge represented a hallmark for bioengineering because the liver, the largest internal organ in the body, is one of the most complex tissues to replicate due to the high number of functions it performs.

Liver tissue created by team Winston for NASA’s Vascular Tissue Challenge.

WAKE FOREST INSTITUTE FOR REGENERATIVE MEDICINE

“When the competition came out six years ago, we knew we had been trying to solve this problem on our own,” says Atala.

Along with advancing the field of regenerative medicine and making it easier to create artificial organs for humans who need transplants, the project could someday help astronauts on future deep-space missions.

The concept of tissue engineering has been around for more than 20 years, says Laura Niklason, a professor of anesthesia and biomedical engineering at Yale, but the growing interest in space-based experimentation is starting to transform the field. “Especially as the world is now looking at private and commercial space travel, the biological impacts of low gravity are going to become more and more important, and this is a great tool for helping to understand that.”

But the winning teams must still overcome one of the biggest hurdles in tissue engineering: “Getting things to survive and maintain their function over an extended period is really challenging,” says Andrea O’Connor, head of biomedical engineering at the University of Melbourne, who calls this project, and others like it ambitious.

Equipped with a $300,000 cash prize, the first-place team—Winston—will soon have a chance to send its research to the International Space Station, where similar organ research has already taken place.

In 2019, astronaut Christina Koch activated the BioFabrication Facility (BFF), which was created by the Greenville, Indiana-based aerospace research company Techshot to print organic tissues in microgravity.

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Bias isn’t the only problem with credit scores—and no, AI can’t help

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Bias isn't the only problem with credit scores—and no, AI can't help


But in the biggest ever study of real-world mortgage data, economists Laura Blattner at Stanford University and Scott Nelson at the University of Chicago show that differences in mortgage approval between minority and majority groups is not just down to bias, but to the fact that minority and low-income groups have less data in their credit histories.

This means that when this data is used to calculate a credit score and this credit score used to make a prediction on loan default, then that prediction will be less precise. It is this lack of precision that leads to inequality, not just bias.

The implications are stark: fairer algorithms won’t fix the problem. 

“It’s a really striking result,” says Ashesh Rambachan, who studies machine learning and economics at Harvard University, but was not involved in the study. Bias and patchy credit records have been hot issues for some time, but this is the first large-scale experiment that looks at loan applications of millions of real people.

Credit scores squeeze a range of socio-economic data, such as employment history, financial records, and purchasing habits, into a single number. As well as deciding loan applications, credit scores are now used to make many life-changing decisions, including decisions about insurance, hiring, and housing.  

To work out why minority and majority groups were treated differently by mortgage lenders, Blattner and Nelson collected credit reports for 50 million anonymized US consumers, and tied each of those consumers to their socio-economic details taken from a marketing dataset, their property deeds and mortgage transactions, and data about the mortgage lenders who provided them with loans.

One reason this is the first study of its kind is that these datasets are often proprietary and not publicly available to researchers. “We went to a credit bureau and basically had to pay them a lot of money to do this,” says Blattner.  

Noisy data

They then experimented with different predictive algorithms to show that credit scores were not simply biased but “noisy,” a statistical term for data that can’t be used to make accurate predictions. Take a minority applicant with a credit score of 620. In a biased system, we might expect this score to always overstate the risk of that applicant and that a more accurate score would be 625, for example. In theory, this bias could then be accounted for via some form of algorithmic affirmative action, such as lowering the threshold for approval for minority applications.

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We investigated whether digital contact tracing actually worked in the US

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We investigated whether digital contact tracing actually worked in the US


In the spring of 2020, the first versions of covid-19 exposure notification systems were released to the public. These systems promised to slow the disease’s spread by providing automated warnings to people who came into contact with the virus. Now, over a year later, residents in over 50 countries—including half of US states—can opt into these systems.

But the big question remains: how well did this technology work? Some studies suggest answers, but despite such wide rollout, it’s difficult to evaluate whether exposure notifications were actually able to stall covid-19 spread. This is especially true in the US, where many states launched their own apps—a decentralized approach that reflects America’s fragmented pandemic response.

In an attempt to learn more about how this technology fared in the US, MIT Technology Review reached out to every state public health department that launched a digital contact tracing system and examined app reviews left by anonymous Americans. We asked two questions: who is actually using this technology, and how do people feel about it?

The end result of this analysis paints a picture of unexplored potential. Many of the country’s exposure notification apps are underutilized, misunderstood, and not well-trusted—and yet this technology may yet come into its own as a public health tool for future disease outbreaks.

How the technology works

Exposure notifications were first put forward as a complement to traditional contact tracing. Under the traditional manual approach, investigators looking for people who may have been infected ask patients to trace their whereabouts and activities through phone calls and interviews. The new technology promised to scale to cover entire populations automatically rather than just small disease clusters— a distinct advantage for tracking a fast-spreading disease.

You might remember the friend you met for lunch, for example, but not the stranger you stood next to in line for 15 minutes at the grocery store. An exposure notification system does the remembering for you, anonymously using Bluetooth to keep a log of nearby phones and alerting you if one of those phones is associated with a positive test result.

The first wave of this system was designed by cooperatives of developers, most of whom ended up collaborating with Apple and Google to create a uniform standard. The Apple-Google system prioritized privacy for users, anonymizing their data, and did not track users locations. With the backing of the world’s two most dominant phone platforms, this system is the one that’s been most widely adopted, and is used by the vast majority of US states. 

The effectiveness of these systems has been notoriously hard to evaluate. Studies are just now starting to come out about apps in the UK and Switzerland, for example. In the US, evaluation is made even harder by the fact that every state is basically doing its own thing. But our analysis does have a few takeaways: 

  • US systems were launched relatively late in the pandemic—when the country’s fall/winter surge was mostly already in progress
  • The technology has not been widely adopted, though some states are faring better than others
  • A lack of public trust in new technology—coupled with a lack of resources in the public health agencies peddling that technology—hampered both adoption rates and how people used the systems

Who’s using this tech

We tracked exposure notification apps that had been rolled out in 25 states and the District of Columbia. Virginia was the first state to make the technology publicly available to its residents in August 2020, while others are still only getting started now. Massachusetts began testing its app with a pilot in two cities in April 2021, while South Carolina is currently running a pilot program at Clemson University. The state actually started work on its system back in May 2020—but legislators barred the public health department from any digital contact tracing work last summer due to privacy concerns, holding back development.

Even in the states where such apps are available, not everybody can use them. Exposure notifications are only available for smartphone users; and about 15% of Americans don’t have a smartphone, according to Pew Research Center. Still, over half of the US population can now get plugged in. Whether they choose to join those systems is another matter.

As the vast majority of states do not publicly report user data, we reached out to state public health departments directly to ask how many people had opted into the technology.

Twenty-four states and DC shared user estimates, showing that, by early May, a total 36.7 million Americans have opted in to the notifications. Hawaii has the highest share of its population covered, at about 46%. In four more states, more than 30% of residents opted in: Connecticut, Maryland, Colorado, and Nevada. Seven more states have over 15% of their populations covered.

That proportion is important: modeling studies have determined that if roughly 15% of a population opts into the system, it could significantly reduce a community’s covid case numbers, hospitalizations, and deaths. By this metric, 13 states—which together represent about one-third of the US population—have seen some degree of protection thanks to exposure notifications.

The remaining 11 states with exposure notification apps fail to meet this benchmark for success. Of those 11, three states have under 5% of their populations covered: Arizona, North Dakota, and Wyoming. South Dakota, the one state which did not respond to a press request, shares use of the Care19 Diary app with the low-activation states of North Dakota and Wyoming.

Comparing states isn’t perfect, though, because there are no federal standards guiding how states collect or report the data—and some may make very different choices to others. For example, while DC reports an “exposure notification opt-in” number on its Reopening Metrics page, this number is actually higher than its residential population. A representative of DC Health explained that the opt-in number includes tourists and people who work in DC, even if they reside elsewhere. For our purposes, we looked at DC’s activation rate as a share of the surrounding metropolitan area’s population (including parts of nearby Maryland, Virginia, and West Virginia).

Another reason these rates are hard to measure: Several of the states with higher usage rates benefit from a major upgrade that Apple and Google released in September: Exposure Notification Express, or ENX. This framework made it much faster for states to spin up apps, and it also invited millions of iPhone users to avoid downloading anything at all. They could activate the notifications simply by flipping a switch in their phone settings.

ENX activation is much more convenient, and experts say it may seem safer than downloading a new app. It has seriously boosted activation rates for states that use it. Hawaii, for example, saw its users more than double from February to May while rolling out ENX. 

The express system does mean we have less precise user data, though. States aren’t able to track ENX activations directly, and instead need to rely on Apple for their numbers.

Beyond the numbers

Even when a lot of residents have downloaded an app or turned that switch in their iPhone settings, the system still needs to be properly used in order to make a difference in covid cases. So we tried to understand how people were using the systems, too. 

A recent study found that Americans were hesitant to trust digital contact tracing technology. However, this finding was based on surveys conducted before most states even launched their apps. As a proxy for public attitudes towards the US state apps, MIT Technology Review scraped and analyzed app reviews from the Google Play store. We only looked at Google Play reviews (from Android users) to get the most current and consistent data. (Most iPhone users can now turn on notifications without downloading an app.)

Looking at app reviews isn’t a perfect system. Users who chose to review their state’s app are not a representative sample of the EN-activating population—instead, they are those users who want to share strong opinions about the technology.

Still, here’s what we found: 

  • Most of the state apps have average ratings between 3 and 4.
  • Michigan has the lowest score, at 2.6.
  • D.C, California, New York, Delaware, and Massachusetts have the highest scores, over 4. 

Many 1-star reviewers appeared to misunderstand how their state’s app works, didn’t trust in the technology, or were unable to understand how the app fit into the broader public health system. This indicates that, for many Americans, the app wasn’t doing its job even though it was technically in use.

Lessons from negative reviews

Poor reviews provide a glimpse into common issues and misconceptions that the digital contact tracing system faced.

Small glitches made a big difference.
Over and over, reviewers stated that they got tripped up by needing an activation code. To help protect privacy, when you test positive for covid you don’t input your name or other identifying details into the app: instead, you enter a string of numbers that your public health department gives you. Some reviewers state that they don’t know where to get an activation code after testing positive, or that they ran into error messages. We’ve heard from developers in other countries about this issue.

Some US states and other countries have streamlined the process by automating how a code gets sent, but in many cases, users must wait for a contact tracer to call them. This waiting period can decrease trust in the technology, and it significantly slows down digital contact tracing.

“Trust” isn’t just about the app itself. It’s broader than that.
Many app reviewers also mistrust new technology, the government, or both. A Pew Research Center survey conducted in July 2020 found that 41% of Americans would likely not speak with a public health official on the phone or via text message, and 27% said they would not be comfortable sharing the names of recent contacts—both key elements of the contact tracing process. 

Digital contact tracing faces similar challenges. Some reviewers felt so strongly about protecting their privacy that they came to their state app’s pages in order to boast about their refusal to download this technology. Many echoed the sentiments of this reviewer from Pennsylvania: “Open access to my wifi, GPS, and Bluetooth? Creepy. No thanks, Harrisburg.”

Low usage creates a downward spiral of mistrust.
One crucial aspect of digital contact tracing is that you need participation for it to work—at least 15% of the community, but preferably much higher. When people aren’t participating, the chance of getting a match is lower—even if covid levels are high—and so the system likely won’t send out alerts to those small number of people who do have exposure notifications activated.

A few reviews went as far as to beg the other residents of their states to opt into exposure notifications, reminding fellow reviewers that higher usage leads to higher effectiveness in a tone that seemed more reminiscent of a Facebook argument than an app store.

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