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Worried about your firm’s AI ethics? These startups are here to help.

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A screenshot of Parity's library of impact assessment questions.


Parity is among a growing crop of startups promising organizations ways to develop, monitor, and fix their AI models. They offer a range of products and services from bias-mitigation tools to explainability platforms. Initially most of their clients came from heavily regulated industries like finance and health care. But increased research and media attention on issues of bias, privacy, and transparency have shifted the focus of the conversation. New clients are often simply worried about being responsible, while others want to “future proof” themselves in anticipation of regulation.

“So many companies are really facing this for the first time,” Chowdhury says. “Almost all of them are actually asking for some help.”

From risk to impact

When working with new clients, Chowdhury avoids using the term “responsibility.” The word is too squishy and ill-defined; it leaves too much room for miscommunication. She instead begins with more familiar corporate lingo: the idea of risk. Many companies have risk and compliance arms, and established processes for risk mitigation.

AI risk mitigation is no different. A company should start by considering the different things it worries about. These can include legal risk, the possibility of breaking the law; organizational risk, the possibility of losing employees; or reputational risk, the possibility of suffering a PR disaster. From there, it can work backwards to decide how to audit its AI systems. A finance company, operating under the fair lending laws in the US, would want to check its lending models for bias to mitigate legal risk. A telehealth company, whose systems train on sensitive medical data, might perform privacy audits to mitigate reputational risk.

Parity includes a library of suggested questions to help companies evaluate the risk of their AI models.

PARITY

Parity helps to organize this process. The platform first asks a company to build an internal impact assessment—in essence, a set of open-ended survey questions about how its business and AI systems operate. It can choose to write custom questions or select them from Parity’s library, which has more than 1,000 prompts adapted from AI ethics guidelines and relevant legislation from around the world. Once the assessment is built, employees across the company are encouraged to fill it out based on their job function and knowledge. The platform then runs their free-text responses through a natural-language processing model and translates them with an eye toward the company’s key areas of risk. Parity, in other words, serves as the new go-between in getting data scientists and lawyers on the same page.

Next, the platform recommends a corresponding set of risk mitigation actions. These could include creating a dashboard to continuously monitor a model’s accuracy, or implementing new documentation procedures to track how a model was trained and fine-tuned at each stage of its development. It also offers a collection of open-source frameworks and tools that might help, like IBM’s AI Fairness 360 for bias monitoring or Google’s Model Cards for documentation.

Chowdhury hopes that if companies can reduce the time it takes to audit their models, they will become more disciplined about doing it regularly and often. Over time, she hopes, this could also open them to thinking beyond risk mitigation. “My sneaky goal is actually to get more companies thinking about impact and not just risk,” she says. “Risk is the language people understand today, and it’s a very valuable language, but risk is often reactive and responsive. Impact is more proactive, and that’s actually the better way to frame what it is that we should be doing.”

A responsibility ecosystem

While Parity focuses on risk management, another startup, Fiddler, focuses on explainability. CEO Krishna Gade began thinking about the need for more transparency in how AI models make decisions while serving as the engineering manager of Facebook’s News Feed team. After the 2016 presidential election, the company made a big internal push to better understand how its algorithms were ranking content. Gade’s team developed an internal tool that later became the basis of the “Why am I seeing this?” feature.

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Reopening US schools is complicated.

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Reopening US schools is complicated.


Across the country, schools are wrestling with the difficult choice of whether to reopen, and how to do it with reduced risk. In Kalamazoo, Michigan—not far from one the main sites where Pfizer is frantically manufacturing vaccines—they plan to stay virtual through the end of the school year. In Iowa, a state without a mask mandate, kids can now go back to in-person learning full time. Meanwhile, in a school district in San Mateo County, California, that borders Silicon Valley, there’s no clear decision—and low-income and affluent parents are clashing over what to do

It’s been a difficult journey. Since March 2020, when most schools closed, districts have been asked to adjust over and over—to new science about how the virus behaves, new policy recommendations, and the different needs of families, kids, teachers, and staff. 

Now, as President Biden forges ahead with his promise to reopen most schools within his first 100 days, the debates sound as complicated as ever—and offer a glimpse into many of the difficulties of reopening society at large. 

The limits of “guidance”

Schools across the country have looked to the Centers for Disease Control and Prevention for guidance on how to operate in the pandemic. In its latest recommendations, the CDC says a lot of the things we’ve heard all year: that everyone in a school building should wear masks, stay at least six feet apart, and wash their hands frequently. But schools have found that even when guidelines seem relatively straightforward on paper, they are often much harder—or downright impossible—to put into practice. 

“There’s a difference between public health mitigation policies when we think them through and when we write them down, and then when we try to implement them,” says Theresa Chapple, an epidemiologist in Washington, DC. “We see that there are barriers at play.”

Chapple points to a recent study by the CDC that looked at elementary schools in Georgia. After just 24 days of in-person learning, the researchers found nine clusters of covid-19 cases that could be linked back to the school. In all, about 45 students and teachers tested positive. How did that happen? Classroom layouts and class sizes meant physical distancing wasn’t possible, so students were less than three feet apart, separated only by plastic dividers. And though students and teachers mostly wore masks, students had to eat lunch in their classrooms. 

Researchers also note that teachers and students may have infected each other “during small group instruction sessions in which educators worked in close proximity to students.”

Following the CDC’s best practices might be inherently difficult, but it’s also complicated by the fact that they are just guidelines: states and other jurisdictions make the rules, and those often conflict with what the CDC says to do. Since February 15, Iowa schools have been required to offer fully in-person learning options that some school officials say make distancing impossible. Because the state no longer has a mask mandate, students aren’t required to wear masks in school.

Jurisdictions following all these different policies have one thing in common: although case totals have dipped since their peak in January, the vast majority of the US still has substantial or high community spread. A big takeaway from the CDC’s latest guidance is that high community transmission is linked to increased risk in schools. 

“If we are opening schools,” Chapple says, “we are saying that there’s an acceptable amount of spread that we will take in order for children to be educated.”

Meeting different needs

Some schools are trying alternative tactics that they hope will reduce the risks associated with in-person learning. 

In Sharon, a Massachusetts town just south of Boston where about 60% of public school students are still learning remotely, pods of students and staff are called down to a central location in their school building twice a week for voluntary covid-19 testing. One by one, children as young as five turn up, sanitize their hands, lower their mask, swab their own nostrils, and place their swab in a single test tube designated for their whole cohort. To make room for everyone, sometimes even the principal’s office becomes a testing site: one person in, one person out. The tubes are then sent to a lab for something called “pooled testing.”

After just 24 days of in-person learning, the researchers found nine clusters of covid-19 cases that could be linked back to the school. 

Pooled testing allows a small group of samples to be tested for covid all at once. In Sharon, each tube holds anywhere from 5 to 25 samples. If the test for that small group comes back negative, the whole group is cleared. If it’s positive, each group member is tested until the positive individual is found. Meg Dussault, the district’s acting superintendent, says each pool test costs the school between $5 and $50, and over a third of Sharon Public Schools students and staff participate. 

“I’ve seen the benefits of this,” she says “And I believe it’s essential.”

Because schools are funded unequally and largely through taxes, access to resources is a common theme in discussions of school reopening. The state paid for Sharon’s pilot period, but not every district or school has the money or staffing to mount large-scale programs—and Dussault says the district will need to foot the bill for any testing once this program ends in April. It will also need to keep relying on the goodwill of the parent volunteers who wrangle students and swabs for testing each week. 

In the seven weeks since pooled testing began, Dussault says, only one batch has come back positive. It’s given her peace of mind.

And even with mitigation measures in place, there are stark demographic differences in opinion on reopening. A recent Pew study found that Black, Asian, and Hispanic adults are more likely to support holding off until teachers have access to vaccines. Those groups are also more likely than white adults to say that the risk of covid-19 transmission “should be given a lot of consideration” when weighing reopening.

Chapple worries that these parents’ concerns will be overlooked, or that funds for remote learning will dwindle because some districts decide to move to in-person learning.

She says: “School districts need to keep in mind that if they’re reopening but a small percentage of their minority students are coming back, what does that look like in terms of equity?” 

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SpaceX has successfully landed Starship after flight for the first time

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SpaceX has successfully landed Starship after flight for the first time


On March 3, SpaceX’s Starship pulled off a successful high-altitude flight—its third in a row. Unlike in the first two missions, the spacecraft stuck the landing. Then, as in the last two, the spacecraft blew up.

What happened: At around 5:14 p.m. US Central Time, the 10th Starship prototype (SN10) was launched from SpaceX’s test facility in Boca Chica, Texas, flying about 10 kilometers into the air before falling back down and descending safely to Earth. 

About 10 minutes later, the spacecraft blew up, from what appears to have been a methane leak. Still, the actual objectives of the mission were met.

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Rocket Lab could be SpaceX’s biggest rival

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Rocket Lab could be SpaceX’s biggest rival


In the private space industry, it can seem that there’s SpaceX and then there’s everyone else. Only Blue Origin, backed by its own billionaire founder in the person of Jeff Bezos, seems able to command the same degree of attention. And Blue Origin hasn’t even gone beyond suborbital space yet. 

Rocket Lab might soon have something to say about that duopoly. The company, founded in New Zealand and headquartered in Long Beach, California, is second only to SpaceX when it comes to launch frequency—the two are ostensibly the only American companies that regularly go to orbit. Its small flagship Electron rocket has flown 18 times in just under four years and delivered almost 100 satellites into space, with only two failed launches. 

On March 1, the company made its ambitions even clearer when it unveiled plans for a new rocket called Neutron. At 40 meters tall and able to carry 20 times the weight that Electron can, Neutron is being touted by Rocket Lab as its entry into markets for large satellite and mega-constellation launches, as well as future robotics missions to the moon and Mars. Even more tantalizing, Rocket Lab says Neutron will be designed for human spaceflight as well. The company calls it a “direct alternative” to the SpaceX Falcon 9 rocket

“Rocket Lab is one of the success stories among the small launch companies,” says Roger Handberg, a space policy expert at the University of Central Florida. “They are edging into the territory of the larger, more established launch companies now—especially SpaceX.”

That ambition was helped by another bit of news announced on March 1: Rocket Lab’s merger with Vector Acquisition Corporation. Joining forces with a special-purpose acquisition company, a type of company that ostensibly enables another business to go public without an IPO, will allow Rocket Lab to benefit from a massive influx of money that gives it a new valuation of $4.1 billion. Much of that money is going toward development and testing of Neutron, which the company wants to start flying in 2024.

It’s a bit of an about-face for Rocket Lab. CEO Peter Beck had previously been lukewarm about the idea of building a larger rocket that could launch bigger payloads and potentially offer launches for multiple customers at once. 

But the satellite market has embraced ride-share missions into orbit, especially given the rise of satellite mega-constellations, which will probably make up most satellites launched into orbit over the next decade. Neutron is capable of taking 8,000 kilograms to low Earth orbit, which means it could deliver potentially dozens of payloads to orbit at once. As a lighthearted mea culpa, the introductory video for Neutron showed Beck eating his own hat. 

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