Paper by Kathleen Elliott Vinson and Samantha A. Moppett: “…While individuals have the constitutional right to legal assistance in criminal cases, the same does not hold true for civil matters. Low-income Americans are unable to gain access to meaningful help for basic legal needs. Although legal aid organizations exist to help low-income Americans who cannot afford legal representation, the resources available are insufficient to meet current civil legal needs. Studies show more than 80 percent of the legal needs of low-income Americans go unaddressed every year.
This article examines how law students, law schools, the legal profession, legal services’ agencies, and low-income individuals who need assistance, all have a shared interest—access to justice—and can work together to reach the elusive goal in the Pledge of Allegiance of “justice for all.” It illustrates how their collaborative leveraging of technology in innovative ways like digital pro bono services, is one way to provide access to justice. It discusses ABA Free Legal Answers Online, the program that the ABA pioneered to help confront the justice gap in the United States. The program provides a “virtual legal advice clinic” where attorneys answer civil legal questions that low-income residents post on free, secure, and confidential state-specific websites. The article provides a helpful resource of how law schools can leverage this technology to increase access to justice for low-income communities while providing pro bono opportunities for attorneys and students in their state…(More)”.
Ben Paynter at Fast Company: “…It’s an idea that’s hard to visualize unless you can see it on a map. So MIT Media Lab collaborated with the location intelligence firm Cuebiqto build one. The result is called the Atlas of Inequality and harvests the anonymized location data from 150,000 people who opted in toCuebiq’s Data For Good Initiative to track their movement for scientific research purposes. After isolating the general area (based on downtime) where each subject lived, MIT Media Lab could estimate what income bracket they occupied. The group then used data from a six-month period between late 2016 and early 2017 to figure out where these people traveled, and how their paths overlapped.
The result is an interactive view of just how filtered, sheltered, or sequestered many people’s lives really are. That’s an important thing to be reminded of at a time when the U.S. feels increasingly ideologically and economically divided. “Economic inequality isn’t just limited to neighborhoods, it’s part of the places you visit every day,” the researchers say in a mission statement about the Atlas….(More)”.
About: “On a typical day in the United States, police officers make more than 50,000 traffic stops. Our team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Our goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.
Currently, a comprehensive, national repository detailing interactions between police and the public doesn’t exist. That’s why the Stanford Open Policing Project is collecting and standardizing data on vehicle and pedestrian stops from law enforcement departments across the country — and we’re making that information freely available. We’ve already gathered 130 million records from 31 state police agencies and have begun collecting data on stops from law enforcement agencies in major cities, as well.
We, the Stanford Open Policing Project, are an interdisciplinary team of researchers and journalists at Stanford University. We are committed to combining the academic rigor of statistical analysis with the explanatory power of data journalism….(More)”.
Marco Lippi et al in AI and the Law Journal: “Terms of service of on-line platforms too often contain clauses that are potentially unfair to the consumer. We present an experimental study where machine learning is employed to automatically detect such potentially unfair clauses. Results show that the proposed system could provide a valuable tool for lawyers and consumers alike….(More)”.
Paper by Rashida Richardson, Jason Schultz, and Kate Crawford: “Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. Yet in numerous jurisdictions, these systems are built on data produced within the context of flawed, racially fraught and sometimes unlawful practices (‘dirty policing’). This can include systemic data manipulation, falsifying police reports, unlawful use of force, planted evidence, and unconstitutional searches. These policing practices shape the environment and the methodology by which data is created, which leads to inaccuracies, skews, and forms of systemic bias embedded in the data (‘dirty data’). Predictive policing systems informed by such data cannot escape the legacy of unlawful or biased policing practices that they are built on. Nor do claims by predictive policing vendors that these systems provide greater objectivity, transparency, or accountability hold up. While some systems offer the ability to see the algorithms used and even occasionally access to the data itself, there is no evidence to suggest that vendors independently or adequately assess the impact that unlawful and bias policing practices have on their systems, or otherwise assess how broader societal biases may affect their systems.
In our research, we examine the implications of using dirty data with predictive policing, and look at jurisdictions that (1) have utilized predictive policing systems and (2) have done so while under government commission investigations or federal court monitored settlements, consent decrees, or memoranda of agreement stemming from corrupt, racially biased, or otherwise illegal policing practices. In particular, we examine the link between unlawful and biased police practices and the data used to train or implement these systems across thirteen case studies. We highlight three of these: (1) Chicago, an example of where dirty data was ingested directly into the city’s predictive system; (2) New Orleans, an example where the extensive evidence of dirty policing practices suggests an extremely high risk that dirty data was or will be used in any predictive policing application, and (3) Maricopa County where despite extensive evidence of dirty policing practices, lack of transparency and public accountability surrounding predictive policing inhibits the public from assessing the risks of dirty data within such systems. The implications of these findings have widespread ramifications for predictive policing writ large. Deploying predictive policing systems in jurisdictions with extensive histories of unlawful police practices presents elevated risks that dirty data will lead to flawed, biased, and unlawful predictions which in turn risk perpetuating additional harm via feedback loops throughout the criminal justice system. Thus, for any jurisdiction where police have been found to engage in such practices, the use of predictive policing in any context must be treated with skepticism and mechanisms for the public to examine and reject such systems are imperative….(More)”.
Joseph Cox at Motherboard: ” In January, Motherboard revealed that AT&T, T-Mobile, and Sprint were selling their customers’ real-time location data, which trickled down through a complex network of companies until eventually ending up in the hands of at least one bounty hunter. Motherboard was also able to purchase the real-time location of a T-Mobile phone on the black market from a bounty hunter source for $300. In response, telecom companies said that this abuse was a fringe case.
In reality, it was far from an isolated incident.
Around 250 bounty hunters and related businesses had access to AT&T, T-Mobile, and Sprint customer location data, with one bail bond firm using the phone location service more than 18,000 times, and others using it thousands or tens of thousands of times, according to internal documents obtained by Motherboard from a company called CerCareOne, a now-defunct location data seller that operated until 2017. The documents list not only the companies that had access to the data, but specific phone numbers that were pinged by those companies.
Some of these bounty hunters then resold location data to those unauthorized to handle it, according to two independent sources familiar with CerCareOne’s operations.
The news shows how widely available Americans’ sensitive location data was to bounty hunters. This ease-of-access dramatically increased the risk of abuse….(More)”.
Karen Hao atMIT Technology Review : “Using historical data to train risk assessment tools could mean that machines are copying the mistakes of the past. …
AI might not seem to have a huge personal impact if your most frequent brush with machine-learning algorithms is through Facebook’s news feed or Google’s search rankings. But at the Data for Black Lives conference last weekend, technologists, legal experts, and community activists snapped things into perspective with a discussion of America’s criminal justice system. There, an algorithm can determine the trajectory of your life. The US imprisons more people than any other country in the world. At the end of 2016, nearly 2.2 million adults were being held in prisons or jails, and an additional 4.5 million were in other correctional facilities. Put another way, 1 in 38 adult Americans was under some form of correctional supervision. The nightmarishness of this situation is one of the few issues that unite politicians on both sides of the aisle. Under immense pressure to reduce prison numbers without risking a rise in crime, courtrooms across the US have turned to automated tools in attempts to shuffle defendants through the legal system as efficiently and safely as possible. This is where the AI part of our story begins….(More)”.
Paperby Daniel L. Chen: “Predictive judicial analytics holds the promise of increasing the fairness of law. Much empirical work observes inconsistencies in judicial behavior. By predicting judicial decisions—with more or less accuracy depending on judicial attributes or case characteristics—machine learning offers an approach to detecting when judges most likely to allow extra legal biases to influence their decision making. In particular, low predictive accuracy may identify cases of judicial “indifference,” where case characteristics (interacting with judicial attributes) do no strongly dispose a judge in favor of one or another outcome. In such cases, biases may hold greater sway, implicating the fairness of the legal system….(More)”
Stacy Wood in KULA: knowledge creation, dissemination, and preservation studies: “This paper considers the history and politics of ‘police data.’ Police data, I contend, is a category of endangered data reliant on voluntary and inconsistent reporting by law enforcement agencies; it is also inconsistently described and routinely housed in systems that were not designed with long-term strategies for data preservation, curation or management in mind. Moreover, whereas US law enforcement agencies have, for over a century, produced and published a great deal of data about crime, data about the ways in which police officers spend their time and make decisions about resources—as well as information about patterns of individual officer behavior, use of force, and in-custody deaths—is difficult to find. This presents a paradoxical situation wherein vast stores of extant data are completely inaccessible to the public. This paradoxical state is not new, but the continuation of a long history co-constituted by technologies, epistemologies and context….(More)”.
Margaret Hagan at Daedalus: “Most access-to-justice technologies are designed by lawyers and reflect lawyers’ perspectives on what people need. Most of these technologies do not fulfill their promise because the people they are designed to serve do not use them. Participatory design, which was developed in Scandinavia as a process for creating better software, brings end users and other stakeholders into the design process to help decide what problems need to be solved and how. Work at the Stanford Legal Design Lab highlights new insights about what tools can provide the assistance that people actually need, and about where and how they are likely to access and use those tools. These participatory design models lead to more effective innovation and greater community engagement with courts and the legal system.
A decade into the push for innovation in access to justice, most efforts reflect the interests and concerns of courts and lawyers rather than the needs of the people the innovations are supposed to serve. New legal technologies and services, whether aiming to help people expunge their criminal records or to get divorced in more cooperative ways, have not been adopted by the general public. Instead, it is primarily lawyers who use them.1
One way to increase the likelihood that innovations will serve clients would be to involve clients in designing them. Participatory design emerged in Scandinavia in the 1970s as a way to think more effectively about decision-making in the workplace. It evolved into a strategy for developing software in which potential users were invited to help define a vision of a product, and it has since been widely used for changing systems like elementary education, hospital services, and smart cities, which use data and technology to improve sustainability and foster economic development.3
Participatory design’s promise is that “system innovation” is more likely to be effective in producing tools that the target group will use and in spending existing resources efficiently to do so. Courts spend an enormous amount of money on information technology every year. But the technology often fails to meet courts’ goals: barely half of the people affected are satisfied with courts’ customer service….(More)”.