They have also been indirect beneficiaries of the insurrection at the Capitol, with spikes in users as a result of the mainstream services’ deplatforming President Trump, his surrogates, and accounts promoting the QAnon conspiracy.
In a few cases, public pressure has forced action. DLive, a cryptocurrency-based video streaming site, which was acquired by BitTorrent’s Tron Foundation in October 2020, suspended or permanently banned accounts, channels, and individual broadcasts after the Southern Poverty Law Center identified those that livestreamed the attack from inside the Capitol building.
Neither Tron Foundation, which owns DLive, nor Medici Ventures, the Overstock subsidiary that invested in Minds, responded to requests for comment.
EvoNexus, a Southern California-based tech incubator that helped fund the self-described “non-biased” social network CloutHub, forwarded our request for comment to CloutHub’s PR team, who denied that its platform was used in the planning of the insurrection. They said that a group started on the platform and promoted by founder Jeff Brain was merely for organizing ride sharing to the Trump rally on January 6. The group, it said, “was for peaceful activities only and asked that members report anyone talking about violence.”
But there’s a fine line between speech and action, says Margaret O’Mara, a historian at the University of Washington who studies the intersection between technology and politics. When, as a platform “you decide you’re not going to take sides, and you’re going to be an unfettered platform for free speech,” and then people “saying horrible things” is “resulting in action,” then platforms need to reckon with the fact that “we are a catalyst of this, we are becoming an organizing platform for this.”
“Maybe you wouldn’t get dealflow”
For the most part, says O’Donnell, investors are worried that expressing an opinion about those companies might limit their ability to make deals, and therefore make money.
Even venture capital firms “have to depend on pools of money elsewhere in the ecosystem,” he says. “The worry was that maybe you wouldn’t get dealflow,” or that you’d be labeled as “difficult to work with or, you know, picking off somebody who might do the next round of your company.”
Despite this, however, O’Donnell says he does not believe that investors should completely avoid “alt tech.” Tech investors like disruption, he explains, and they see in alt tech the potential to “break up the monoliths.”
“Could that same technology be used for coordinating among people in doing bad stuff? Yeah, it’s possible, just in the same way that people use phones to commit crimes,” he says, adding that this issue can be resolved by having the right rules and procedures in place.
“There’s some alternative tech whose DNA is about decentralization, and there’s some alt-tech whose DNA is about a political perspective,” he says. He does not consider Gab, for example, to be a decentralized platform, but rather “a central hosting hub for people who otherwise violate the terms of service of other platforms.”
“The internet is decentralized, right? But we have means for creating databases of bad actors, when it comes to spam, when it comes to denial of service attacks,” he says, suggesting the same could be true of bad actors on alt tech platforms.
But overlooking the more dangerous sides of these communications platforms, and how their design often facilitates dangerous behavior is a mistake, says O’Mara. “It’s a kind of escapism that runs through the response that powerful people in tech … have, which is just, if we have alternative technologies, if we just have a decentralized internet, if we just have Bitcoin” … then everything will be better.
She calls this position “idealistic” but “very unrealistic,” and a reflection of “a deeply rooted piece of Silicon Valley culture. It goes all the way back to, ‘We don’t like the world as it is, so we’re gonna build this alternative platform on which to revise social relationships.’”
The problem, O’Mara adds, is that these solutions are “very technology driven” and “chiefly promulgated by pretty privileged people that … have a hard time … [imagining] a lot of the social politics. So there’s not a real reckoning with structural inequality or other systems that need to be changed.”
How to have “a transformational effect”
Some believe that tech investors could shift what kind of companies get built, if they chose to.
“If venture capitalists committed to not investing in predatory business models that incite violence, that would have a transformational effect,” says McNamee.
At an individual level, they could ask better questions even before investing, says O’Donnell, including avoiding companies without content policies, or requesting that companies create them before a VC signs on.
Once invested, O’Donnell adds that investors can also sell their shares, including at a loss, if they truly wanted to take a stand. But he recognizes the tall order that this would represent—after all, it’s highly likely that a high-growth startup will simply find a different source of money to step in to the space that a principled investor just vacated. “It’s going to be pissing in the wind,” he says, “Because that guy over there is going to be in.”
In other words, a real reckoning among VCs would require a reorientation of how Silicon Valley thinks, and right now it is still focused on “one, and only one, metric that matters, and that’s financial return,” says Freada Kapor Klein.
If funders changed their investment strategies—to put in moral clauses against companies that profit from extremism, for example, as O’Donnell suggested—the impact that this would have on what startup founders chase would be enormous, says O’Mara. “People follow the money,” she says, but “it’s not just money, it’s mentorship, it’s how you build a company, it’s this whole set of principles about what success looks like.”
“It would have been great if VCs who pride themselves on risk-taking and innovation and disruption … led the way,” concludes Kapor Klein. “But this tsunami is coming. And they will have to change.”
Correction: Brooklyn Bridge Ventures is an investor in Clubhouse, a product management software company, not Clubhouse, the social network as originally stated.
NASA inches closer to printing artificial organs in space
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.
“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.
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.
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.
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.