揽瓜阁俱乐部第三期 Day4 2020.07.23
【社会科学-科技】 AI can predict which criminals may break laws again better than humans （710字 精读 必做篇）
Computer algorithms can outperform people at predicting which criminals will get arrested again, a new study finds.
Risk-assessment algorithms that forecast future crimes often help judges and parole boards decide who stays behind bars. But these systems have come under fire for exhibiting racial biases , and some research has given reason to doubt that algorithms are any better at predicting arrests than humans are. One 2018 study that pitted human volunteers against the risk-assessment tool COMPAS found that people predicted criminal reoffence about as well as the software.
The new set of experiments confirms that humans predict repeat offenders about as well as algorithms when the people are given immediate feedback on the accuracy of their predications and when they are shown limited information about each criminal. But people are worse than computers when individuals don’t get feedback, or if they are shown more detailed criminal profiles.
In reality, judges and parole boards don’t get instant feedback either, and they usually have a lot of information to work with in making their decisions. So the study’s findings suggest that, under realistic prediction conditions, algorithms outmatch people at forecasting recidivism, researchers report online February 14 in Science Advances.
Computational social scientist Sharad Goel of Stanford University and colleagues started by mimicking the setup of the 2018 study. Online volunteers read short descriptions of 50 criminals — including features like sex, age and number of past arrests — and guessed whether each person was likely to be arrested for another crime within two years. After each round, volunteers were told whether they guessed correctly. As seen in 2018, people rivaled COMPAS’s performance: accurate about 65 percent of the time.
But in a slightly different version of this human vs. computer competition, Goel’s team found that COMPAS had an edge over people who did not receive feedback. In this experiment, participants had to predict which of 50 criminals would be arrested for violentcrimes, rather than just any crime.
With feedback, humans performed this task with 83 percent accuracy — close to COMPAS’ 89 percent. But without feedback, human accuracy fell to about 60 percent. That’s because people overestimated the risk of criminals committing violent crimes, despite being told that only 11 percent of the criminals in the dataset fell into this camp, the researchers say. The study did not investigate whether factors such as racial or economic biases contributed to that trend.
In a third variation of the experiment, risk-assessment algorithms showed an upper hand when given more detailed criminal profiles. This time, volunteers faced off against a risk-assessment tool dubbed LSI-R. That software could consider 10 more risk factors than COMPAS, including substance abuse, level of education and employment status. LSI-R and human volunteers rated criminals on a scale from very unlikely to very likely to reoffend.
When shown criminal profiles that included only a few risk factors, volunteers performed on par with LSI-R. But when shown more detailed criminal descriptions, LSI-R won out. The criminals with highest risk of getting arrested again, as ranked by people, included 57 percent of actual repeat offenders, whereas LSI-R’s list of most probable arrestees contained about 62 percent of actual reoffenders in the pool. In a similar task that involved predicting which criminals would not only get arrested, but re-incarcerated, humans’ highest-risk list contained 58 percent of actual reoffenders, compared with LSI-R’s 74 percent.
Computer scientist Hany Farid of the University of California, Berkeley, who worked on the 2018 study, is not surprised that algorithms eked out an advantage when volunteers didn’t get feedback and had more information to juggle. But just because algorithms outmatch untrained volunteers doesn’t mean their forecasts should automatically be trusted to make criminal justice decisions, he says.
Eighty percent accuracy might sound good, Farid says, but “you’ve got to ask yourself, if you’re wrong 20 percent of the time, are you willing to tolerate that?”
Since neither humans nor algorithms show amazing accuracy at predicting whether someone will commit a crime two years down the line, “should we be using [those forecasts] as a metric to determine whether somebody goes free?” Farid says. “My argument is no.”
Perhaps other questions, like how likely someone is to get a job or jump bail, should factor more heavily into criminal justice decisions, he suggests.
Source: Science News
【社会科学-科技】 Lab-Grown Human Mini Brains Show Brainy Activity （432字 2分54秒 精听 必做篇）
先做精听再核对原文哦~
It’s not easy to study the early development of the human brain.
“The brain is very inaccessible, especially the early fetal stages. It’s just not ethical to study normal, healthy human brains.”
University of California, San Diego, biologist Alysson Muotri. He says researchers have instead relied on animal models.
“But the human brain is so much different from other species that we’re desperate to have, really, a human model so we can study the human brain.”
Now Muotri’s team may have that model, in the form of small globules of brain cells they’ve created in the lab. These pea-sized structures develop from stem cells that are bathed in a culture of nutrients, along with proteins that control gene activation. As the little structures grow, their constituents also specialize into different types of brain cells.
“And they will form connections, and these connections will form functional synapses that will, later on, turn into networks.”
After two months, the mini brains even begin to emit brain waves.
“And you can record every week to see how the activity has changed. And when they reach about six months of age, we see a growth exponentially in the number of connections and synapses that they can make.”
And at around 10 months, their brain activity compares to that of premature human infants.
“They’re pretty much following the same trajectory as the human brain does.”
That could make the mini brains very useful for understanding how our brains become wired early on. And they could also provide insights into the development of neurological conditions such as autism and epilepsy.
“These very early stages are exactly when some neurological conditions appear. And we have the possibility to help millions of people with neurological conditions.”
But Muotri also cautions that as the technology moves forward, ethical questions will start to emerge.
“Someone might ask, ‘Are they conscious or are they self-aware? Can they feel pain?’ I think we are in a gray zone, where this technology could evolve to something more complex. And then I think the ethical question would be, ‘What’s the moral status of these miniaturized brains?’”
Muotri says that same question has formed the basis for the rules and regulations governing the use of animals in the lab, which can serve as a model to guide the mini brain research. The findings are in the journal Cell Stem Cell.
In addition to shedding light on neurological development, mini brains could also help reveal how the human brain evolved and play a role in improving algorithms for artificial intelligence. These pea-sized brains may produce some big insights.
Source: Scientific American
【笔记格式要求】
精读笔记格式要求： 1.总结文章中心大意 2.总结分论点或每段段落大意 3.摘抄印象深刻或者觉得优美的句子 4.总结文章中的生词 5.记录阅读时间、总结时间、总时间
精听笔记格式要求： 1.逐句听写整篇文章 2.对照原文修改听写稿，标记出错原因 3.总结文章中心大意 4.总结精听过程中的生词 5.记录听写时间、总结时间、总时间
这里也给大家两点学习小建议哦~ 精读：如遇到读不懂的复杂句，建议找出句子主干，分析句子成分，也可以尝试翻译句子来帮助理解~ 精听：建议每句不要反复纠结听，如果听 5 遍都没听出来，那就跳过，等完成后再回听总结原因，时间宝贵，不要过于执着哦~
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发表于 2020-7-22 22:24:26
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