In her testimony, Haugen also repeatedly emphasized how these phenomena are far worse in regions that don’t speak English because of Facebook’s uneven coverage of different languages.
“In the case of Ethiopia there are 100 million people and six languages. Facebook only supports two of those languages for integrity systems,” she said. “This strategy of focusing on language-specific, content-specific systems for AI to save us is doomed to fail.”
She continued: “So investing in non-content-based ways to slow the platform down not only protects our freedom of speech, it protects people’s lives.”
I explore this more in a different article from earlier this year on the limitations of large language models, or LLMs:
Despite LLMs having these linguistic deficiencies, Facebook relies heavily on them to automate its content moderation globally. When the war in Tigray[, Ethiopia] first broke out in November, [AI ethics researcher Timnit] Gebru saw the platform flounder to get a handle on the flurry of misinformation. This is emblematic of a persistent pattern that researchers have observed in content moderation. Communities that speak languages not prioritized by Silicon Valley suffer the most hostile digital environments.
Gebru noted that this isn’t where the harm ends, either. When fake news, hate speech, and even death threats aren’t moderated out, they are then scraped as training data to build the next generation of LLMs. And those models, parroting back what they’re trained on, end up regurgitating these toxic linguistic patterns on the internet.
How does Facebook’s content ranking relate to teen mental health?
One of the more shocking revelations from the Journal’s Facebook Files was Instagram’s internal research, which found that its platform is worsening mental health among teenage girls. “Thirty-two percent of teen girls said that when they felt bad about their bodies, Instagram made them feel worse,” researchers wrote in a slide presentation from March 2020.
Haugen connects this phenomenon to engagement-based ranking systems as well, which she told the Senate today “is causing teenagers to be exposed to more anorexia content.”
“If Instagram is such a positive force, have we seen a golden age of teenage mental health in the last 10 years? No, we have seen escalating rates of suicide and depression amongst teenagers,” she continued. “There’s a broad swath of research that supports the idea that the usage of social media amplifies the risk of these mental health harms.”
In my own reporting, I heard from a former AI researcher who also saw this effect extend to Facebook.
The researcher’s team…found that users with a tendency to post or engage with melancholy content—a possible sign of depression—could easily spiral into consuming increasingly negative material that risked further worsening their mental health.
But as with Haugen, the researcher found that leadership wasn’t interested in making fundamental algorithmic changes.
The team proposed tweaking the content-ranking models for these users to stop maximizing engagement alone, so they would be shown less of the depressing stuff. “The question for leadership was: Should we be optimizing for engagement if you find that somebody is in a vulnerable state of mind?” he remembers.
But anything that reduced engagement, even for reasons such as not exacerbating someone’s depression, led to a lot of hemming and hawing among leadership. With their performance reviews and salaries tied to the successful completion of projects, employees quickly learned to drop those that received pushback and continue working on those dictated from the top down….
That former employee, meanwhile, no longer lets his daughter use Facebook.
How do we fix this?
Haugen is against breaking up Facebook or repealing Section 230 of the US Communications Decency Act, which protects tech platforms from taking responsibility for the content it distributes.
Instead, she recommends carving out a more targeted exemption in Section 230 for algorithmic ranking, which she argues would “get rid of the engagement-based ranking.” She also advocates for a return to Facebook’s chronological news feed.
Facebook wants machines to see the world through our eyes
For the last two years, Facebook AI Research (FAIR) has worked with 13 universities around the world to assemble the largest ever data set of first-person video—specifically to train deep-learning image-recognition models. AIs trained on the data set will be better at controlling robots that interact with people, or interpreting images from smart glasses. “Machines will be able to help us in our daily lives only if they really understand the world through our eyes,” says Kristen Grauman at FAIR, who leads the project.
Such tech could support people who need assistance around the home, or guide people in tasks they are learning to complete. “The video in this data set is much closer to how humans observe the world,” says Michael Ryoo, a computer vision researcher at Google Brain and Stony Brook University in New York, who is not involved in Ego4D.
But the potential misuses are clear and worrying. The research is funded by Facebook, a social media giant that has recently been accused in the US Senate of putting profits over people’s well-being—as corroborated by MIT Technology Review’s own investigations.
The business model of Facebook, and other Big Tech companies, is to wring as much data as possible from people’s online behavior and sell it to advertisers. The AI outlined in the project could extend that reach to people’s everyday offline behavior, revealing what objects are around your home, what activities you enjoyed, who you spent time with, and even where your gaze lingered—an unprecedented degree of personal information.
“There’s work on privacy that needs to be done as you take this out of the world of exploratory research and into something that’s a product,” says Grauman. “That work could even be inspired by this project.”
The biggest previous data set of first-person video consists of 100 hours of footage of people in the kitchen. The Ego4D data set consists of 3,025 hours of video recorded by 855 people in 73 different locations across nine countries (US, UK, India, Japan, Italy, Singapore, Saudi Arabia, Colombia, and Rwanda).
The participants had different ages and backgrounds; some were recruited for their visually interesting occupations, such as bakers, mechanics, carpenters, and landscapers.
Previous data sets typically consisted of semi-scripted video clips only a few seconds long. For Ego4D, participants wore head-mounted cameras for up to 10 hours at a time and captured first-person video of unscripted daily activities, including walking along a street, reading, doing laundry, shopping, playing with pets, playing board games, and interacting with other people. Some of the footage also includes audio, data about where the participants’ gaze was focused, and multiple perspectives on the same scene. It’s the first data set of its kind, says Ryoo.
This NASA spacecraft is on its way to Jupiter’s mysterious asteroid swarms
Lucy will take black-and-white and color images, and use a diamond beam splitter to shine far-infrared light at the asteroids to take their temperature and make maps of their surface. It will also collect other measurements as it flies by. This data could help scientists understand how the planets may have formed.
Sarah Dodson-Robinson, an assistant professor of physics and astronomy at the University of Delaware, says Lucy could offer a definitive time line for not only when the planets originally formed, but where.
“If you can nail down when the Trojan asteroids formed, then you have some information about when did Jupiter form, and can start asking questions like ‘Where did Jupiter go in the solar system?’” she says. “Because it wasn’t always where it is now. It’s moved around.”
And to determine the asteroids’ ages, the spacecraft will search for surface craters that may be no bigger than a football field.
“[The Trojans] haven’t had nearly as much colliding and breaking as some of the other asteroids that are nearer to us,” says Dodson-Robinson. “We’re potentially getting a look at some of these asteroids like they were shortly after they formed.”
On its 4-billion-mile journey, Lucy will receive three gravity assists from Earth, which will involve using the planet’s gravitational force to change the spacecraft’s trajectory without depleting its resources. Coralie Adam, deputy navigation team chief for the Lucy mission, says each push will increase the spacecraft’s velocity from 200 miles per hour to over 11,000 mph.
“If not for this Earth gravity assist, it would take five times the amount of fuel—or three metric tons—to reach Lucy’s target, which would make the mission unfeasible,” said Adam during an engineering media briefing also held on October 14.
Lucy’s mission is slated to end in 2033, but some NASA officials already feel confident that the spacecraft will last far longer. “There will be a good amount of fuel left onboard,” said Adam. “After the final encounter with the binary asteroids, as long as the spacecraft is healthy, we plan to propose to NASA to do an extended mission and explore more Trojans.”
Reimagining our pandemic problems with the mindset of an engineer
The last 20 months turned every dog into an amateur epidemiologist and statistician. Meanwhile, a group of bona fide epidemiologists and statisticians came to believe that pandemic problems might be more effectively solved by adopting the mindset of an engineer: that is, focusing on pragmatic problem-solving with an iterative, adaptive strategy to make things work.
In a recent essay, “Accounting for uncertainty during a pandemic,” the researchers reflect on their roles during a public health emergency and on how they could be better prepared for the next crisis. The answer, they write, may lie in reimagining epidemiology with more of an engineering perspective and less of a “pure science” perspective.
Epidemiological research informs public health policy and its inherently applied mandate for prevention and protection. But the right balance between pure research results and pragmatic solutions proved alarmingly elusive during the pandemic.
We have to make practical decisions, so how much does the uncertainty really matter?
“I always imagined that in this kind of emergency, epidemiologists would be useful people,” Jon Zelner, a coauthor of the essay, says. “But our role has been more complex and more poorly defined than I had expected at the outset of the pandemic.” An infectious disease modeler and social epidemiologist at the University of Michigan, Zelner witnessed an “insane proliferation” of research papers, “many with very little thought about what any of it really meant in terms of having a positive impact.”
“There were a number of missed opportunities,” Zelner says—caused by missing links between the ideas and tools epidemiologists proposed and the world they were meant to help.
Giving up on certainty
Coauthor Andrew Gelman, a statistician and political scientist at Columbia University, set out “the bigger picture” in the essay’s introduction. He likened the pandemic’s outbreak of amateur epidemiologists to the way war makes every citizen into an amateur geographer and tactician: “Instead of maps with colored pins, we have charts of exposure and death counts; people on the street argue about infection fatality rates and herd immunity the way they might have debated wartime strategies and alliances in the past.”
And along with all the data and public discourse—Are masks still necessary? How long will vaccine protection last?—came the barrage of uncertainty.
In trying to understand what just happened and what went wrong, the researchers (who also included Ruth Etzioni at the University of Washington and Julien Riou at the University of Bern) conducted something of a reenactment. They examined the tools used to tackle challenges such as estimating the rate of transmission from person to person and the number of cases circulating in a population at any given time. They assessed everything from data collection (the quality of data and its interpretation were arguably the biggest challenges of the pandemic) to model design to statistical analysis, as well as communication, decision-making, and trust. “Uncertainty is present at each step,” they wrote.
And yet, Gelman says, the analysis still “doesn’t quite express enough of the confusion I went through during those early months.”
One tactic against all the uncertainty is statistics. Gelman thinks of statistics as “mathematical engineering”—methods and tools that are as much about measurement as discovery. The statistical sciences attempt to illuminate what’s going on in the world, with a spotlight on variation and uncertainty. When new evidence arrives, it should generate an iterative process that gradually refines previous knowledge and hones certainty.
Good science is humble and capable of refining itself in the face of uncertainty.
Susan Holmes, a statistician at Stanford who was not involved in this research, also sees parallels with the engineering mindset. “An engineer is always updating their picture,” she says—revising as new data and tools become available. In tackling a problem, an engineer offers a first-order approximation (blurry), then a second-order approximation (more focused), and so on.
Gelman, however, has previously warned that statistical science can be deployed as a machine for “laundering uncertainty”—deliberately or not, crappy (uncertain) data are rolled together and made to seem convincing (certain). Statistics wielded against uncertainties “are all too often sold as a sort of alchemy that will transform these uncertainties into certainty.”
We witnessed this during the pandemic. Drowning in upheaval and unknowns, epidemiologists and statisticians—amateur and expert alike—grasped for something solid as they tried to stay afloat. But as Gelman points out, wanting certainty during a pandemic is inappropriate and unrealistic. “Premature certainty has been part of the challenge of decisions in the pandemic,” he says. “This jumping around between uncertainty and certainty has caused a lot of problems.”
Letting go of the desire for certainty can be liberating, he says. And this, in part, is where the engineering perspective comes in.
A tinkering mindset
For Seth Guikema, co-director of the Center for Risk Analysis and Informed Decision Engineering at the University of Michigan (and a collaborator of Zelner’s on other projects), a key aspect of the engineering approach is diving into the uncertainty, analyzing the mess, and then taking a step back, with the perspective “We have to make practical decisions, so how much does the uncertainty really matter?” Because if there’s a lot of uncertainty—and if the uncertainty changes what the optimal decisions are, or even what the good decisions are—then that’s important to know, says Guikema. “But if it doesn’t really affect what my best decisions are, then it’s less critical.”
For instance, increasing SARS-CoV-2 vaccination coverage across the population is one scenario in which even if there is some uncertainty regarding exactly how many cases or deaths vaccination will prevent, the fact that it is highly likely to decrease both, with few adverse effects, is motivation enough to decide that a large-scale vaccination program is a good idea.
An engineer is always updating their picture.
Engineers, Holmes points out, are also very good at breaking problems down into critical pieces, applying carefully selected tools, and optimizing for solutions under constraints. With a team of engineers building a bridge, there is a specialist in cement and a specialist in steel, a wind engineer and a structural engineer. “All the different specialties work together,” she says.
For Zelner, the notion of epidemiology as an engineering discipline is something he picked up from his father, a mechanical engineer who started his own company designing health-care facilities. Drawing on a childhood full of building and fixing things, his engineering mindset involves tinkering—refining a transmission model, for instance, in response to a moving target.
“Often these problems require iterative solutions, where you’re making changes in response to what does or doesn’t work,” he says. “You continue to update what you’re doing as more data comes in and you see the successes and failures of your approach. To me, that’s very different—and better suited to the complex, non-stationary problems that define public health—than the kind of static one-and-done image a lot of people have of academic science, where you have a big idea, test it, and your result is preserved in amber for all time.”