New tech for prison reform spreads to 11 states
A new non-profit called Recidiviz is using data technology to reduce the size of the U.S. criminal justice system. The bi-coastal company (SF and NYC) is currently working with 11 states to improve their systems and, so far, has helped remove nearly 69,000 people — ones left floundering in jail or on parole when they should have been released.
“The root cause is fragmentation,” says Clementine Jacoby, 31, a software engineer who worked at Google before co-founding Recidiviz in 2019. In the 1970s and 80s, the U.S. built a series of disconnected data systems, and this patchwork is still being used by criminal justice authorities today. It requires parole officers to manually calculate release dates, leading to errors in many cases. “[They] have done everything they need to do to earn their release, but they're still stuck in the system,” Jacoby says.
Recidiviz has built a platform that connects the different databases, with the goal of identifying people who are already qualified for release but remain behind bars or on supervision. “Think of Recidiviz like Google Maps,” says Jacoby, who worked on Maps when she was at the tech giant. Google Maps takes in data from different sources – satellite images, street maps, local business data — and organizes it into one easy view. “Recidiviz does something similar with criminal justice data,” Jacoby explains, “making it easy to identify people eligible to come home or to move to less intensive levels of supervision.”
People like Jacoby’s uncle. His experience with incarceration is what inspired her passion for criminal justice reform in the first place.
The problems are vast
The U.S. has the highest incarceration rate in the world — 2 million people according to the watchdog group, Prison Policy Initiative — at a cost of $182 billion a year. The numbers could be a lot lower if not for an array of problems including inaccurate sentencing calculations, flawed algorithms and parole violations laws.
Sentencing miscalculations
To determine eligibility for release, the current system requires corrections officers to check 21 different requirements spread across five different databases for each of the 90 to 100 people under their supervision. These manual calculations are time prohibitive, says Jacoby, and fall victim to human error.
In addition, Recidiviz found that policies aimed at helping to reduce the prison population don’t always work correctly. A key example is time off for good behavior laws that allow inmates to earn one day off for every 30 days of good behavior. Some states' data systems are built to calculate time off as one day per month of good behavior, rather than per day. Over the course of a decade-long sentence, Jacoby says these miscalculations can lead to a huge discrepancy in the calculated release data and the actual release date.
Algorithms
Commercial algorithm-based software systems for risk assessment continue to be widely used in the criminal justice system, even though a 2018 study published in Science Advances exposed their limitations. After the study went viral, it took three years for the Justice Department to issue a report on their own flawed algorithms used to reduce the federal prison population as part of the 2018 First Step Act. The program, it was determined, overestimated the risk of putting inmates of color into early-release programs.
Despite its name, Recidiviz does not build these types of algorithms for predicting recidivism, or whether someone will commit another crime after being released from prison. Rather, Jacoby says the company’s "descriptive analytics” approach is specifically intended to weed out incarceration inequalities and avoid algorithmic pitfalls.
Parole violation laws
Research shows that 350,000 people a year — about a quarter of the total prison population — are sent back not because they’ve committed another crime, but because they’ve broken a specific rule of their probation. “Things that wouldn't send you or I to prison, but would send someone on parole,” such as crossing county lines or being in the presence of alcohol when they shouldn’t be, are inflating the prison population, says Jacoby.
It’s personal for the co-founder and CEO
“I grew up with an uncle who went into the prison system,” Jacoby says. At 19, he was sentenced to ten years in prison for a non-violent crime. A few months after being released from jail, he was sent back for a non-violent parole violation.
“For my family, the fact that one in four prison admissions are driven not by a crime but by someone who's broken a rule on probation and parole was really profound because that happened to my uncle,” Jacoby says. The experience led her to begin studying criminal justice in high school, then college. She continued her dive into how the criminal justice system works as part of her Passion Project while at Google, a program that allows employees to spend 20 percent of their time on pro-bono work. Two colleagues whose family members had also been stuck in the system joined her.
As part of the project, Jacoby interviewed hundreds of people involved in the criminal justice system. “Those on the right, those on the left, agreed that bad data was slowing down reform,” she says. Their research brought them to North Dakota where they began to understand the root of the problem. The corrections department is making “huge, consequential decisions every day [without] … the data,” Jacoby says. In a new video by Recidiviz not yet released, Jacoby recounts her exchange with the state’s director of corrections who told her, “‘It’s not that we have the data and we just don’t know how to make it public; we don’t have the information you think we have.'"
A mock-up (with fake data) of the types of dashboards and insights that Recidiviz provides to state governments.
Recidiviz
As a software engineer, Jacoby says the comment made no sense to her — until she witnessed it first-hand. “We spent a lot of time driving around in cars with corrections directors and parole officers watching them use these incredibly taxing, frankly terrible, old data systems,” Jacoby says.
As they weeded through thousands of files — some computerized, some on paper — they unearthed the consequences of bad data: Hundreds of people in prison well past their release date and thousands more whose release from parole was delayed because of minor paperwork issues. They found individuals stuck in parole because they hadn’t checked one last item off their eligibility list — like simply failing to provide their parole officer with a paystub. And, even when parolees advocated for themselves, the archaic system made it difficult for their parole officers to confirm their eligibility, so they remained in the system. Jacoby and her team also unpacked specific policies that drive racial disparities — such as fines and fees.
The Solution
It’s more than a trivial technical challenge to bring the incomplete, fragmented data onto a 21st century data platform. It takes months for Recidiviz to sift through a state’s information systems to connect databases “with the goal of tracking a person all the way through their journey and find out what’s working for 18- to 25-year-old men, what’s working for new mothers,” explains Jacoby in the video.
TED Talk: How bad data traps people in the U.S. justice system
TED Fellow Clementine Jacoby's TED Talk went live on Jan. 13. It describes how we can fix bad data in the criminal justice system, "bringing thousands of people home, reducing costs and improving public safety along the way."
Clementine Jacoby • TED2022
Ojmarrh Mitchell, an associate professor in the School of Criminology and Criminal Justice at Arizona State University, who is not involved with the company, says what Recidiviz is doing is “remarkable.” His perspective goes beyond academic analysis. In his pre-academic years, Mitchell was a probation officer, working within the framework of the “well known, but invisible” information sharing issues that plague criminal justice departments. The flexibility of Recidiviz’s approach is what makes it especially innovative, he says. “They identify the specific gaps in each jurisdiction and tailor a solution for that jurisdiction.”
On the downside, the process used by Recidiviz is “a bit opaque,” Mitchell says, with few details available on how Recidiviz designs its tools and tracks outcomes. By sharing more information about how its actions lead to progress in a given jurisdiction, Recidiviz could help reformers in other places figure out which programs have the best potential to work well.
The eleven states in which Recidiviz is working include California, Colorado, Maine, Michigan, Missouri, Pennsylvania and Tennessee. And a pilot program launched last year in Idaho, if scaled nationally, with could reduce the number of people in the criminal justice system by a quarter of a million people, Jacoby says. As part of the pilot, rather than relying on manual calculations, Recidiviz is equipping leaders and the probation officers with actionable information with a few clicks of an app that Recidiviz built.
Mitchell is disappointed that there’s even the need for Recidiviz. “This is a problem that government agencies have a responsibility to address,” he says. “But they haven’t.” For one company to come along and fill such a large gap is “remarkable.”
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
Podcast: Wellness chatbots and meditation pods with Deepak Chopra
Over the last few decades, perhaps no one has impacted healthy lifestyles more than Deepak Chopra. While several of his theories and recommendations have been criticized by prominent members of the scientific community, he has helped bring meditation, yoga and other practices for well-being into the mainstream in ways that benefit the health of vast numbers of people every day. His work has led many to accept new ways of thinking about alternative medicine, the power of mind over body, and the malleability of the aging process.
His impact is such that it's been observed our culture no longer recognizes him as a human being but as a pervasive symbol of new-agey personal health and spiritual growth. Last week, I had a chance to confirm that Chopra is, in fact, a human being – and deserving of his icon status – when I talked with him for the Leaps.org podcast. He relayed ideas that were wise and ancient, yet highly relevant to our world today, with the fluidity and ease of someone discussing the weather. Showing no signs of slowing down at age 76, he described his prolific work, including the publication of two books in the past year and a range of technologies he’s developing, including a meditation app, meditation pods for the workplace, and a chatbot for mental health called Piwi.
Take a listen and get inspired to do some meditation and deep thinking on the future of health. As Chopra told me, “If you don’t have time to meditate once per day, you probably need to meditate twice per day.”
Highlights:
2:10: Chopra talks about meditation broadly and meditation pods, including the ones made by OpenSeed for meditation in the workplace.
6:10: The drawbacks of quick fixes like drugs for mental health.
10:30: The benefits of group meditation versus individual meditation.
14:35: What is a "metahuman" and how to become one.
19:40: The difference between the conditioned mind and the mind that's infinitely creative.
22:48: How Chopra's views of free will differ from the views of many neuroscientists.
28:04: Thinking Fast and Slow, and the role of intuition.
31:20: Athletic and creative geniuses.
32:43: The nature of fundamental truth.
34:00: Meditation for kids.
37:12: Never alone.Love and how AI chatbots can support mental health.
42:30: Extending lifespan, gene editing and lifestyle.
46:05: Chopra's mentor in living a long good life (and my mentor).
47:45: The power of yoga.
Links:
- OpenSeed meditation pods for people to meditate at work (Chopra is an advisor to OpenSeed).
- Chopra's book from 2021, Metahuman: Unleash Your Infinite Potential
- Chopra's book from 2022, Abundance: The Inner Path to Wealth
- NeverAlone.Love, Chopra's collaboration of businesses, policy makers, mental health professionals and others to raise awareness about mental health, advance scientific research and "create a global technology platform to democratize access to resources."
- The Piwi chatbot for mental health
- The Chopra Meditation & Well-Being App for people of all ages
- Only 1.6 percent of U.S. children meditate, according to the National Center for Complementary and Integrative Health