Salinas High School sophomore Stephanie Tena, 16, is passionate about coding and AI, and she is working on a project that uses these skills to detect contamination in water sources.Alison Yin

During her freshman year, Stephanie Tena, a 16-year-old programmer, was searching the internet for coding programs and came across a website for an organization called AI4All, which runs an artificial-intelligence summer camp for high-schoolers. On the site, a group of girls her age were gathered around an autonomous car in front of the iconic arches of Stanford’s campus. “AI will change the world,” the text read. “Who will change AI?”

Tena thought maybe she could. She lives in a trailer park in California’s Salinas Valley; her mom, a Mexican immigrant from Michoacán, picks strawberries in the nearby fields.* Tena has long black hair, a cheery, high-pitched voice, and an unflappably professional bearing: She refers to other students as “my peers” and her mentors as “notable professors,” and she has nailed the language of the scientific method (“My hypothesis was proven incorrect”). She had been coding for a couple of years, ever since attending a programming club at the local community college when she was still in junior high. “I prefer science over history,” she says. The summer after the 8th grade, she had flown to Los Angeles for a coding boot camp run by the supermodel Karlie Kloss, (who had gotten interested in coding after taking a class herself), where she had learned some programming languages and developed a website. She saved up for more than a year, both her allowance and her pay from working at a bubble-tea shop, and bought a new MacBook.

But even as Tena applied, and was accepted, to the AI4All program on a full scholarship, she knew little about artificial intelligence. Nor was she fully aware of AI4All’s reason for existing: While women make up only a quarter of computer scientists, their numbers appear to be even smaller in the AI field in particular. While there are no government statistics on the percentage of women in AI, women at the Annual Conference on Neural Information Processing Systems (NIPS), the AI field’s top conference, made up only 17 percent of attendees last year. The percentage of women has risen for the last four years, and NIPS is considering changing the anatomically evocative conference title “in the context of diversity issues,” according to its website.

Artificial intelligence is considered the major driver of what’s known as the fourth industrial revolution (after the steam engine, electricity and mass production, and the digital eras), with major tech companies like Google, Facebook, Amazon, and Microsoft realigning around it. Algorithms are driving ever more real-world decisions: helping doctors detect cancer; suggesting who should be released from jail, interviewed for a job, or get a loan. While some high-profile technologists, such as Elon Musk, have expressed fears that AI could become an existential threat to humanity, others in the field have identified a more immediate concern: far from some God-like omniscience, AI can be as biased and fallible as the humans who build it. AI has already made embarrassing mistakes, like when Google Photos auto-tagged pictures of two black people as gorillas earlier this year because the algorithm, it seems, wasn’t good at correctly labeling some non-white faces. An Uber self-driving car killed a pedestrian in Arizona. While women were fighting for full sexual agency in the real world, mostly male roboticists were creating AI-enhanced mostly female sexbots. Bringing people like Stephanie Tena into artificial intelligence is not simply important for the tech industry; in a world increasingly driven by algorithms, it’s important for all of us.

Stephanie Tena, 16, left, teaches middle schooler Bianca Castro, 12, coding and AI during an after- school club at Washington Irving Middle School. (Alison Yin)

In 2014, Fei-Fei Li, an associate professor at Stanford, started brainstorming about how to widen the pipeline with Olga Russakovsky, an advisee at Stanford, who was a research assistant in the AI sub-field known as computer vision, which involves analyzing images. Russakovsky now teaches at Princeton. In 2015, they held the first summer camp on the Stanford campus (then called Stanford Artificial Intelligence Laboratory’s Outreach Summer Program or SAILORS), a two-week immersion program for girls in the basics of artificial intelligence. Eventually, Li, Russakovsky, and Rick Sommer, who runs Stanford’s pre-college summer programs, created a nonprofit with a board of advisors from academia, nonprofits, and the industry, and funding from the likes of Melinda Gates and Autodesk. They hired Tess Posner, who had led initiatives aimed at getting more diversity into the digital economy, to be the CEO. “A lot of the perception of AI is that it’s so hard to do and exclusive and you need to be a genius,” says Posner. “And this program is helping to break that narrative and say this is really for anyone and has applications for helping people.” This summer, AI4All will take place on six campuses in the United States and Canada—some campuses focusing on girls, and others on students of color and low-income students.

The first summer, Li recalls, the students lit up when, in the middle of a dense computer-science lecture, she described a project in which she and colleagues had used computer vision to track hand hygiene practices among staff at a Stanford hospital, so as to minimize the spread of infections. “Even though they’re only 14 or 15 years old,” she says, “they’re passionate about making a difference in using whatever they learned.” One alumna went on to work with Li on a project using computer vision to assess a surgeon’s operating skill; the resulting paper won best paper at a top AI conference. Another started a hackathon for female high-school students.

Middle schooler Karman Kaur learns coding and AI during an after-school club run by Stephanie Tena. (Alison Yin)

The program was in its second summer, last June, when Tena’s sister dropped her off at Stanford. Tena walked into a room of girls who hailed from Silicon Valley, other areas of the country, and as far away as Pakistan. She was one of very few bilingual Spanish speakers, and the only one from the agricultural regions of California. She was a little intimidated when she learned other girls already knew the Java and Python programming languages—“They had a little bit of a head start”—but she plunged into the curriculum. She was inspired that Li had come to the United States at the age of 16 without knowing English and had risen to become a top AI researcher; along with teaching at Stanford, Li is now a Google executive. Tena also loved the finer points of campus life, namely, the tater tots at the university cafeteria. By the end of two weeks, Tena and a group of teammates were programming a mini-autonomous car.

Tena left the program with an awareness not only of AI but of the way in which the field threatens to shut out people from communities like hers. “ If there’s a project going on, and the majority of the people in the project are of one race or one gender, you’re not really able to have the perspective of others, so it will be tailored or targeted towards one specific group,” she says. When she returned to Salinas, she started an AI club at the local public junior high. She walks over each Monday afternoon to teach the basics of Java—which, while once so foreign to her, has become the one she’s most skilled at—and about major figures like Alan Turing, and the process for coding websites. The composition of her club, as she calculates it, is more than half girls, and more than three-quarters racial minorities. In her own public high school, she is one of only a few females in the school’s inaugural advanced-placement computer-science class. “I have an A,” she says.

Under the supervision of her former AVID teacher Estefany Reyes, left, Stephanie Tena leads an after-school club to teach AI to middle schoolers. (Alison Yin)

Tena also signed up for a new program AI4All debuted in the spring, called the Alumni Research Fellowship Program, which paired students with industry mentors to pursue real-world AI projects. Tena decided on a data-science project to map the water toxicity in her area, given that the Salinas Valley’s water is contaminated from fertilizer and manure run-off. She wanted to see how that data correlates with the demographics of the surrounding towns—to test if, for example, poor areas have dirtier water. One requirement of the program was that Tena meet twice with a mentor in Oakland. Tena’s older sister, who lives near Sacramento, would drive two-and-a-half hours south to Salinas to pick her up, take her to the meetings, then chaperone her home again—eight hours of driving each time.


On a recent Saturday afternoon, the research fellowship’s 13 students gathered in the Wozniak Lounge in Berkeley’s engineering building to present their projects. A procession of teen girls, and one boy, approached a podium in the front of the room, wearing blazers and dresses, as their parents filmed them on their smartphones, and a photo collage of Steve Wozniak, an Apple founder, looked on. One girl was working on a tool to detect wildfires using a drone. Another had developed a speech bot that detected and responded to abusive language. One team of three had created a triage system for paramedics to respond to the most serious calls first; one team member explained that her grandmother had died after suffering a stroke last year when the ambulance took longer than 20 minutes to reach her. At the end, Russakovsky said, “I was coming here expecting we kind of played around with some data, and, no, these are, like, real research presentations!”

Speaking after the ceremony, the group on the paramedic project giggled that they’d just gotten their algorithm finally working the previous night. “I’m not going to lie, I was also surprised by the quality of our presentation,” said Viansa Schmulbach, a student from Portola Valley.* “We didn’t have working code at all; it was calling everything a diabetic emergency.” Their mentor, a female software engineer from IBM’s Watson group, sat down next to the team and handed them congratulatory laptop stickers.

Most of the students were Asian Americans from high-achieving Silicon Valley high schools with rigorous science and math curricula; a few told me their parents were doctors and engineers. Some said they’d been in computer-science camps and classes before AI4All, but that still didn’t mean they’d seen themselves going into the computer-science field. “The only person I knew who codes is my brother,” Trisha Sengupta, a student from a San Jose public high school, told me. Until recently, she’d thought she’d probably go into medicine. Now, after the project, Sengupta is pondering a double major in biomedical science and computer science. “Now there’s like a repository on GitHub which has my name on it,” she told me. “Okay, that’s cool.”

Programs like AI4All are no doubt drawing dozens of girls, people of color, and low-income teens into a field they otherwise wouldn’t have considered—which, in combination with other coding-focused camps, such as Black Girls Code, may start to improve diversity. Still, privately-run coding camps are not as scalable or omnipresent as, say, getting a basic coding curriculum into all junior high and high schools: The tech industry isn’t missing women and minorities in the dozens, but the tens of thousands.

At the end of the ceremony, Li stood and asked for feedback from the mentors. In the spirit of programming, she is keen on rapidly iterating to improve the program. “I see high- school students today being a lot more overscheduled than I was when I was in high school,” said one Pandora data engineer, eliciting knowing laughs from the parents. “It was difficult to make progress when there’s so many other competing things going on.” The engineer wondered whether it would be better run as a hackathon, and one mom said she wished the program had taken place during the summer, with less going on. Another mentor chimed in that she liked the multi-week format of the program: Since the students on her team arrived with differing levels of AI knowledge, it gave her time to help get them all up to speed.

Stephanie Tena is working on a project to detect contamination in water sources as a result of runoff from fields in her hometown. (Alison Yin)

One student wasn’t there to chime in: Stephanie Tena. Her mentor had fallen absent as the weeks wore on, and she didn’t have enough help to complete the project. So she was glumly stuck in Salinas. Still, AI4All was working on transferring her to another mentor, and she would present her water project to the high-school girls at this year’s Stanford camp. She’ll already be a familiar face: On the AI4All web page Tena first landed on, her photo is now at the top


*This post originally misstated the region where Stephanie Tena is from.

*This post originally misnamed the student speaking about the paramedic project. We regret the error.

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