Could a self-monitoring system for criminals replace prisons one day? Future Chronicles is our regular speculative look at inventions yet to come. In this latest installment, we journey to 2050, when technology had been developed so that criminals could be monitored at home. "It's no surprise that the first countries to abolish prisons were Scandinavian " In the 2020s, the US was spending an eye-watering $182 billion a year on locking up its citizens. No other country imprisoned as many people or spent as much in doing so.

Many incarcerated individuals face significant and complex challenges, including mental illness, substance dependence, and homelessness, yet jails and prisons are often poorly equipped to address these needs. With little support from the existing criminal justice system, these needs can remain untreated and worsen, often leading to further offenses and a cycle of incarceration with adverse outcomes both for the individual and for public safety, with particularly large impacts on communities of color that continue to widen the already extensive racial disparities in criminal justice outcomes. Responding to these failures, a growing number of criminal justice stakeholders are seeking to break this cycle through innovative approaches such as community-driven and alternative approaches to policing, mentoring, community building, restorative justice, pretrial diversion, holistic defense, and social service connections. Here we report on a collaboration between Johnson County, Kansas, and Carnegie Mellon University to perform targeted, proactive mental health outreach in an effort to reduce reincarceration rates. This paper describes the data used, our predictive modeling approach and results, as well as the design and analysis of a field trial conducted to confirm our model's predictive power, evaluate the impact of this targeted outreach, and understand at what level of reincarceration risk outreach might be most effective. Through this trial, we find that our model is highly predictive of new jail bookings, with more than half of individuals in the trial's highest-risk group returning to jail in the following year. Outreach was most effective among these highest-risk individuals, with impacts on mental health utilization, EMS dispatches, and criminal justice involvement.

Social Determinants of Health (SDOH) play a significant role in patient health outcomes. The Center of Disease Control (CDC) introduced a subset of ICD-10 codes called Z-codes in an attempt to officially recognize and measure SDOH in the health care system. However, these codes are rarely annotated in a patient's Electronic Health Record (EHR), and instead, in many cases, need to be inferred from clinical notes. Previous research has shown that large language models (LLMs) show promise on extracting unstructured data from EHRs. However, with thousands of models to choose from with unique architectures and training sets, it's difficult to choose one model that performs the best on coding tasks. Further, clinical notes contain trusted health information making the use of closed-source language models from commercial vendors difficult, so the identification of open source LLMs that can be run within health organizations and exhibits high performance on SDOH tasks is an urgent problem. Here, we introduce an intelligent routing system for SDOH coding that uses a language model router to direct medical record data to open source LLMs that demonstrate optimal performance on specific SDOH codes. The intelligent routing system exhibits state of the art performance of 97.4% accuracy averaged across 5 codes, including homelessness and food insecurity, on par with closed models such as GPT-4o. In order to train the routing system and validate models, we also introduce a synthetic data generation and validation paradigm to increase the scale of training data without needing privacy protected medical records. Together, we demonstrate an architecture for intelligent routing of inputs to task-optimal language models to achieve high performance across a set of medical coding sub-tasks.

Predictive modeling of criminal recidivism, or whether people will re-offend in the future, has a long and contentious history. Modern causal inference methods allow us to move beyond prediction and target the "treatment effect" of a specific intervention on an outcome in an observational dataset. In this paper, we look specifically at the effect of incarceration (prison time) on recidivism, using a well-known dataset from North Carolina. Two popular causal methods for addressing confounding bias are explained and demonstrated: directed acyclic graph (DAG) adjustment and double machine learning (DML), including a sensitivity analysis for unobserved confounders. We find that incarceration has a detrimental effect on recidivism, i.e., longer prison sentences make it more likely that individuals will re-offend after release, although this conclusion should not be generalized beyond the scope of our data. We hope that this case study can inform future applications of causal inference to criminal justice analysis.

Akemi Leung knew her grandfather had been incarcerated at Heart Mountain in Wyoming during World War II. But he never spoke much about it. Only when she read and watched a video of his testimony at a congressional commission hearing did she learn more about what he suffered as one of more than 120,000 Americans of Japanese ancestry forced to leave their homes and live in concentration camps. "I just knew him to be a quiet person who liked to observe more than talk," Leung said. "Seeing the testimony helped illustrate how he was a leader."

This paper explores how different ideas of racial equity in machine learning, in justice settings in particular, can present trade-offs that are difficult to solve computationally. Machine learning is often used in justice settings to create risk assessments, which are used to determine interventions, resources, and punitive actions. Overall aspects and performance of these machine learning-based tools, such as distributions of scores, outcome rates by levels, and the frequency of false positives and true positives, can be problematic when examined by racial group. Models that produce different distributions of scores or produce a different relationship between level and outcome are problematic when those scores and levels are directly linked to the restriction of individual liberty and to the broader context of racial inequity. While computation can help highlight these aspects, data and computation are unlikely to solve them. This paper explores where values and mission might have to fill the spaces computation leaves.

The rest of the thread, Tell me about a paper that you found inspiring, from u/mitare is also quite interesting. This paper is a really comprehensive review detailing what exactly current ML techniques are unable to do that humans can do very well. It lays the groundwork that needs to be done to make human-level artificial intelligence.

Dan Hunter is a prison guard's worst nightmare. But he's not a hardened crim. As dean of Swinburne University's Law School, he's working to have most wardens replaced by a system of advanced artificial intelligence connected to a network of high-tech sensors. Called the Technological Incarceration Project, the idea is to make not so much an internet of things as an internet of incarceration. Professor Hunter's team is researching an advanced form of home detention, using artificial intelligence, machine-learning algorithms and lightweight electronic sensors to monitor convicted offenders on a 24-hour basis.

Dan Hunter is a prison guard's worst nightmare. But he's not a hardened crim. As dean of Swinburne University's Law School, he's working to have most wardens replaced by a system of advanced artificial intelligence connected to a network of high-tech sensors. Called the Technological Incarceration Project, the idea is to make not so much an internet of things as an internet of incarceration. Professor Hunter's team is researching an advanced form of home detention, using artificial intelligence, machine-learning algorithms and lightweight electronic sensors to monitor convicted offenders on a 24-hour basis.

Taxpayers spend 39 billion a year on jailing 2.3 million people, making the U.S. the country with the highest incarceration rates in the world. And while technology is radically reshaping every aspect of our economy and society, none of our advances in computing and data are helping to stem the tide of mass incarceration. At a workshop in the capital last Tuesday, White House senior adviser Lynn Overmann of the Office of Science and Technology Policy called on the technologists of the country to figure out how to use data and technology to end widespread incarceration, according to Government Technology. Overmann wants artificial intelligence and machine learning programs that improve screening processes, scan body camera footage for police misconduct and make sentencing more fair. "I represented a client who was looking at spending 40 years of his life in prison because he stole a lawnmower and a weed-eater from a shed in a backyard," she said.