Bigwigs in a more than 600-year-old South American population were easy to spot. Their artificially elongated, teardrop-shaped heads screamed prestige, a new study finds.

During the 300 years before the Incas’ arrival in 1450, intentional head shaping among prominent members of the Collagua ethnic community in Peru increasingly centered on a stretched-out look, says bioarchaeologist Matthew Velasco of Cornell University. Having long, narrow noggins cemented bonds among members of a power elite — a unity that may have helped pave a relatively peaceful incorporation into the Incan Empire, Velasco proposes in the February Current Anthropology.
“Increasingly uniform head shapes may have encouraged a collective identity and political unity among Collagua elites,” Velasco says. These Collagua leaders may have negotiated ways to coexist with the encroaching Inca rather than fight them, he speculates. But the fate of the Collaguas and a neighboring population, the Cavanas, remains hazy. Those populations lived during a conflict-ridden time — after the collapse of two major Andean societies around 1100 (SN: 8/1/09, p. 16) and before the expansion of the Inca Empire starting in the 15th century.

For at least the past several thousand years, human groups in various parts of the world have intentionally modified skull shapes by wrapping infants’ heads with cloth or binding the head between two pieces of wood (SN: 4/29/17, p. 18). Researchers generally assume that this practice signified membership in ethnic or kin groups, or perhaps social rank.
The Callagua people lived in Colca Valley in southeastern Peru and raised alpaca for wool. By tracking Collagua skull shapes over 300 years, Velasco found that elongated skulls became increasingly linked to high social status. By the 1300s, for instance, Collagua women with deliberately distended heads suffered much less skull damage from physical attacks than other females did, he reports. Chemical analyses of bones indicates that long-headed women ate a particularly wide variety of foods.
Until now, knowledge of head-shaping practices in ancient Peru primarily came from Spanish accounts written in the 1500s. Those documents referred to tall, thin heads among Collaguas and wide, long heads among Cavanas, implying that a single shape had always characterized each group.

“Velasco has discovered that the practice of cranial modification was much more dynamic over time and across social [groups],” says bioarchaeologist Deborah Blom of the University of Vermont in Burlington.

Velasco examined 211 skulls of mummified humans interred in either of two Collagua cemeteries. Burial structures built against a cliff face were probably reserved for high-ranking individuals, whereas common burial grounds in several caves and under nearby rocky overhangs belonged to regular folk.
Radiocarbon analyses of 13 bone and sediment samples allowed Velasco to sort Collagua skulls into early and late pre-Inca groups. A total of 97 skulls, including all 76 found in common burial grounds, belonged to the early group, which dated to between 1150 and 1300. Among these skulls, 38 — or about 39 percent — had been intentionally modified. Head shapes included sharply and slightly elongated forms as well as skulls compressed into wide, squat configurations.

Of the 14 skulls with extreme elongation, 13 came from low-ranking individuals, a pattern that might suggest regular folk first adopted elongated head shapes. But with only 21 skulls from elites, the finding may underestimate the early frequency of elongated heads among the high-status crowd. Various local groups may have adopted their own styles of head modification at that time, Velasco suggests.

In contrast, among 114 skulls from elite burial sites in the late pre-Inca period, dating to between 1300 and 1450, 84 — or about 74 percent — displayed altered shapes. A large majority of those modified skulls — about 64 percent — were sharply elongated. Shortly before the Incas’ arrival, prominent Collaguas embraced an elongated style as their preferred head shape, Velasco says. No skeletal evidence has been found to determine whether low-ranking individuals also adopted elongated skulls as a signature look in the late pre-Inca period.

In courtrooms around the United States, computer programs give testimony that helps decide who gets locked up and who walks free.

These algorithms are criminal recidivism predictors, which use personal information about defendants — like family and employment history — to assess that person’s likelihood of committing future crimes. Judges factor those risk ratings into verdicts on everything from bail to sentencing to parole.

Computers get a say in these life-changing decisions because their crime forecasts are supposedly less biased and more accurate than human guesswork.
But investigations into algorithms’ treatment of different demographics have revealed how machines perpetuate human prejudices. Now there’s reason to doubt whether crime-prediction algorithms can even boast superhuman accuracy.

Computer scientist Julia Dressel recently analyzed the prognostic powers of a widely used recidivism predictor called COMPAS. This software determines whether a defendant will commit a crime within the next two years based on six defendant features — although what features COMPAS uses and how it weighs various data points is a trade secret.

Dressel, who conducted the study while at Dartmouth College, recruited 400 online volunteers, who were presumed to have little or no criminal justice expertise. The researchers split their volunteers into groups of 20, and had each group read descriptions of 50 defendants. Using such information as sex, age and criminal history, the volunteers predicted which defendants would reoffend.
A comparison of the volunteers’ answers with COMPAS’ predictions for the same 1,000 defendants found that both were about 65 percent accurate. “We were like, ‘Holy crap, that’s amazing,’” says study coauthor Hany Farid, a computer scientist at Dartmouth. “You have this commercial software that’s been used for years in courts around the country — how is it that we just asked a bunch of people online and [the results] are the same?”

There’s nothing inherently wrong with an algorithm that only performs as well as its human counterparts. But this finding, reported online January 17 in Science Advances, should be a wake-up call to law enforcement personnel who might have “a disproportionate confidence in these algorithms,” Farid says.

“Imagine you’re a judge, and I tell you I have this highly secretive, highly proprietary, expensive software built on big data, and it says the person standing in front of you is high risk” for reoffending, he says. “The judge would be like, ‘Yeah, that sounds quite serious.’ But now imagine if I tell you, ‘Twenty people online said this person is high risk.’ I imagine you’d weigh that information a little bit differently.” Maybe these predictions deserve the same amount of consideration.

Judges could get some better perspective on recidivism predictors’ performance if the Department of Justice or National Institute for Standards and Technology established a vetting process for new software, Farid says. Researchers could test computer programs against a large, diverse dataset of defendants and OK algorithms for courtroom use only if they get a passing grade for prediction.

Farid has his doubts that computers can show much improvement. He and Dressel built several simple and complex algorithms that used two to seven defendant features to predict recidivism. Like COMPAS, all their algorithms maxed out at about D-level accuracy. That makes Farid wonder whether trying to predict crime with anything approaching A+ accuracy is an exercise in futility.

“Maybe there will be huge breakthroughs in data analytics and machine learning over the next decade that [help us] do this with a high accuracy,” he says. But until then, humans may make better crime predictors than machines. After all, if a bunch of average Joe online recruits gave COMPAS a run for its money, criminal justice experts — like social workers, parole officers, judges or detectives — might just outperform the algorithm.

Even if computer programs aren’t used to predict recidivism, that doesn’t mean they can’t aid law enforcement, says Chelsea Barabas, a media researcher at MIT. Instead of creating algorithms that use historic crime data to predict who will reoffend, programmers could build algorithms that examine crime data to find trends that inform criminal justice research, Barabas and colleagues argue in a paper to be presented at the Conference on Fairness, Accountability and Transparency in New York City on February 23.

For instance, if a computer program studies crime statistics and discovers that certain features — like a person’s age or socioeconomic status — are highly related to repeated criminal activity, that could inspire new studies to see whether certain interventions, like therapy, help those at-risk groups. In this way, computer programs would do one better than just predict future crime. They could help prevent it.

Engineers are taking a counterintuitive approach to protecting future spacecraft: shooting at their experiments. The image above and high-speed video below capture a 2.8-millimeter aluminum bullet plowing through a test material for a space shield at 7 kilometers per second. The work is an effort to find structures that could stand up to the impact of space debris.

Earth is surrounded by a cloud of debris, both natural — such as micrometeorites and comet dust, which create meteor showers — and unnatural, including dead satellites and the cast-off detritus of space launches. Those pieces of flotsam can damage other spacecraft if they collide at high speeds, and bits smaller than about a centimeter are hard to track and avoid, says ESA materials engineer Benoit Bonvoisin in a statement.
To defend future spacecraft from taking a hit, Bonvoisin and colleagues are developing armor made from fiber metal laminates, or several thin metal layers bonded together. The laminates are arranged in multiple layers separated by 10 to 30 centimeters, a configuration called a Whipple shield.

In this experiment at the Fraunhofer Institute for High-Speed Dynamics in Germany, the first layer shatters the aluminum bullet into a cloud of smaller pieces, which the second layer is able to deflect. This configuration has been used for decades, but the materials are new. The next step is to test the shield in orbit with a small CubeSat, Bonvoisin says.