Artificial intelligence is here. It’s overhyped, poorly understood, and flawed but already core to our lives—and it’s only going to extend its reach.
AI powers driverless car research, spots otherwise invisible signs of disease on medical images, finds an answer when you ask Alexa a question, and lets you unlock your phone with your face to talk to friends as an animated poop on the iPhone X using Apple’s Animoji. Those are just a few ways AI already touches our lives, and there’s plenty of work still to be done. But don’t worry, superintelligent algorithms aren’t about to take all the jobs or wipe out humanity.
The current boom in all things AI was catalyzed by breakthroughs in an area known as machine learning. It involves “training” computers to perform tasks based on examples, rather than relying on programming by a human. A technique called deep learning has made this approach much more powerful. Just ask Lee Sedol, holder of 18 international titles at the complex game of Go. He got creamed by software called AlphaGo in 2016.
There’s evidence that AI can make us happier and healthier. But there’s also reason for caution. Incidents in which algorithms picked up or amplified societal biases around race or gender show that an AI-enhanced future won’t automatically be a better one.
The Beginnings of Artificial Intelligence
Artificial intelligence as we know it began as a vacation project. Dartmouth professor John McCarthy coined the term in the summer of 1956, when he invited a small group to spend a few weeks musing on how to make machines do things like use language.
He had high hopes of a breakthrough in the drive toward human-level machines. “We think that a significant advance can be made,” he wrote with his co-organizers, “if a carefully selected group of scientists work on it together for a summer.”
Those hopes were not met, and McCarthy later conceded that he had been overly optimistic. But the workshop helped researchers dreaming of intelligent machines coalesce into a recognized academic field.
Early work often focused on solving fairly abstract problems in math and logic. But it wasn’t long before AI started to show promising results on more human tasks. In the late 1950s, Arthur Samuel created programs that learned to play checkers. In 1962, one scored a win over a master at the game. In 1967, a program called Dendral showed it could replicate the way chemists interpreted mass-spectrometry data on the makeup of chemical samples.
As the field of AI developed, so did different strategies for making smarter machines. Some researchers tried to distill human knowledge into code or come up with rules for specific tasks, like understanding language. Others were inspired by the importance of learning to understand human and animal intelligence. They built systems that could get better at a task over time, perhaps by simulating evolution or by learning from example data. The field hit milestone after milestone as computers mastered tasks that could previously only be completed by people.
Deep learning, the rocket fuel of the current AI boom, is a revival of one of the oldest ideas in AI. The technique involves passing data through webs of math loosely inspired by the working of brain cells that are known as artificial neural networks. As a network processes training data, connections between the parts of the network adjust, building up an ability to interpret future data.
Artificial neural networks became an established idea in AI not long after the Dartmouth workshop. The room-filling Perceptron Mark 1 from 1958, for example, learned to distinguish different geometric shapes and got written up in The New York Times as the “Embryo of Computer Designed to Read and Grow Wiser.” But neural networks tumbled from favor after an influential 1969 book coauthored by MIT’s Marvin Minsky suggested they couldn’t be very powerful.
Not everyone was convinced by the skeptics, however, and some researchers kept the technique alive over the decades. They were vindicated in 2012, when a series of experiments showed that neural networks fueled with large piles of data could give machines new powers of perception. Churning through so much data was difficult using traditional computer chips, but a shift to graphics cards precipitated an explosion in processing power.
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In 2018, while the Argentine Congress was hotly debating whether to decriminalize abortion, the Ministry of Early Childhood in the northern province of Salta and the American tech giant Microsoft presented an algorithmic system to predict teenage pregnancy. They called it the Technology Platform for Social Intervention.
“With technology you can foresee five or six years in advance, with first name, last name, and address, which girl—future teenager—is 86 percent predestined to have an adolescent pregnancy,” Juan Manuel Urtubey, then the governor of the province, proudly declared on national television. The stated goal was to use the algorithm to predict which girls from low-income areas would become pregnant in the next five years. It was never made clear what would happen once a girl or young woman was labeled as “predestined” for motherhood or how this information would help prevent adolescent pregnancy. The social theories informing the AI system, like its algorithms, were opaque.
The system was based on data—including age, ethnicity, country of origin, disability, and whether the subject’s home had hot water in the bathroom—from 200,000 residents in the city of Salta, including 12,000 women and girls between the ages of 10 and 19. Though there is no official documentation, from reviewing media articles and two technical reviews, we know that “territorial agents” visited the houses of the girls and women in question, asked survey questions, took photos, and recorded GPS locations. What did those subjected to this intimate surveillance have in common? They were poor, some were migrants from Bolivia and other countries in South America, and others were from Indigenous Wichí, Qulla, and Guaraní communities.
Although Microsoft spokespersons proudly announced that the technology in Salta was “one of the pioneering cases in the use of AI data” in state programs, it presents little that is new. Instead, it is an extension of a long Argentine tradition: controlling the population through surveillance and force. And the reaction to it shows how grassroots Argentine feminists were able to take on this misuse of artificial intelligence.
In the 19th and early 20th centuries, successive Argentine governments carried out a genocide of Indigenous communities and promoted immigration policies based on ideologies designed to attract European settlement, all in hopes of blanquismo, or “whitening” the country. Over time, a national identity was constructed along social, cultural, and most of all racial lines.
This type of eugenic thinking has a propensity to shapeshift and adapt to new scientific paradigms and political circumstances, according to historian Marisa Miranda, who tracks Argentina’s attempts to control the population through science and technology. Take the case of immigration. Throughout Argentina’s history, opinion has oscillated between celebrating immigration as a means of “improving” the population and considering immigrants to be undesirable and a political threat to be carefully watched and managed.
More recently, the Argentine military dictatorship between 1976 and 1983 controlled the population through systematic political violence. During the dictatorship, women had the “patriotic task” of populating the country, and contraception was prohibited by a 1977 law. The cruelest expression of the dictatorship’s interest in motherhood was the practice of kidnapping pregnant women considered politically subversive. Most women were murdered after giving birth and many of their children were illegally adopted by the military to be raised by “patriotic, Catholic families.”
While Salta’s AI system to “predict pregnancy” was hailed as futuristic, it can only be understood in light of this long history, particularly, in Miranda’s words, the persistent eugenic impulse that always “contains a reference to the future” and assumes that reproduction “should be managed by the powerful.”
Due to the complete lack of national AI regulation, the Technology Platform for Social Intervention was never subject to formal review and no assessment of its impacts on girls and women has been made. There has been no official data published on its accuracy or outcomes. Like most AI systems all over the world, including those used in sensitive contexts, it lacks transparency and accountability.
Though it is unclear whether the technology program was ultimately suspended, everything we know about the system comes from the efforts of feminist activists and journalists who led what amounted to a grassroots audit of a flawed and harmful AI system. By quickly activating a well-oiled machine of community organizing, these activists brought national media attention to how an untested, unregulated technology was being used to violate the rights of girls and women.
“The idea that algorithms can predict teenage pregnancy before it happens is the perfect excuse for anti-women and anti-sexual and reproductive rights activists to declare abortion laws unnecessary,” wrote feminist scholars Paz Peña and Joana Varon at the time. Indeed, it was soon revealed that an Argentine nonprofit called the Conin Foundation, run by doctor Abel Albino, a vocal opponent of abortion rights, was behind the technology, along with Microsoft.
For decades, doctors and hospitals saw kidney patients differently based on their race. A standard equation for estimating kidney function applied a correction for Black patients that made their health appear rosier, inhibiting access to transplants and other treatments.
On Thursday, a task force assembled by two leading kidney care societies said the practice is unfair and should end.
The group, a collaboration between the National Kidney Foundation and the American Society of Nephrology, recommended use of a new formula that does not factor in a patient’s race. In a statement, Paul Palevsky, the foundation’s president, urged “all laboratories and health care systems nationwide to adopt this new approach as rapidly as possible.” That call is significant because recommendations and guidelines from professional medical societies play a powerful role in shaping how specialists care for patients.
A study published in 2020 that reviewed records for 57,000 people in Massachusetts found that one-third of Black patients would have had their disease classified as more severe if they had been assessed using the same version of the formula as white patients. The traditional kidney calculation was an example of a class of medical algorithms and calculators that have recently come under fire for conditioning patient care based on race, which is a social category not biological one.
A review published last year listed more than a dozen such tools, in areas such as cardiology and cancer care. It helped prompt a surge of activism against the practice from diverse groups, including medical students and lawmakers such as Senator Elizabeth Warren (D-Massachusetts) and the chair of the House Ways and Means Committee, Richard Neal (D-Massachusetts).
Recently there are signs the tide is turning. The University of Washington dropped the use of race in kidney calculations last year after student protests led to a reconsideration of the practice. Mass General Brigham and Vanderbilt hospitals also abandoned the practice in 2020.
In May, a tool used to predict the chance a woman who previously had a cesarean section could safely give birth via vaginal delivery was updated to no longer automatically assign lower scores to Black and Hispanic women. A calculator that estimates the chances a child has a urinary tract infection was updated to no longer slash the scores for patients who are Black.
The prior formula for assessing kidney disease, known as CKD-EPI, was introduced in 2009, updating a 1999 formula that used race in a similar way. It converts the level of a waste product called creatinine in a person’s blood into a measure of overall kidney function called estimated glomerular filtration rate, or eGFR. Doctors use eGFR to help classify the severity of a person’s illness and determine if they qualify for various treatments, including transplants. Healthy kidneys produce higher scores.
The equation’s design factored in a person’s age and sex but also boosted the score of any patient classified as Black by 15.9 percent. That feature was included to account for statistical patterns seen in the patient data used to inform the design of CKD-EPI, which included relatively few people who were Black or from other racial minorities. But it meant a person’s perceived race could shift how their disease was measured or treated. A person with both Black and white heritage, for example, could flip a health system’s classification of their illness depending on how their doctor saw them or how they identified.
Nwamaka Eneanya, an assistant professor at University of Pennsylvania and a member of the task force behind Thursday’s recommendation, says she knows of one biracial patient with severe kidney disease who after learning about how the equation worked requested that she be classified as white to increase her chances of being listed for advanced care. Eneanya says a shift away from the established equation is long overdue. “Using someone’s skin color to guide their clinical pathway is wholeheartedly wrong—you introduce racial bias into medical care when you do that,” she says.
