Brazil hires OpenAI to cut costs of court battles


Article by Marcela Ayres and Bernardo Caram: “Brazil’s government is hiring OpenAI to expedite the screening and analysis of thousands of lawsuits using artificial intelligence (AI), trying to avoid costly court losses that have weighed on the federal budget.

The AI service will flag to government the need to act on lawsuits before final decisions, mapping trends and potential action areas for the solicitor general’s office (AGU).

AGU told Reuters that Microsoft would provide the artificial intelligence services from ChatGPT creator OpenAI through its Azure cloud-computing platform. It did not say how much Brazil will pay for the services.

Court-ordered debt payments have consumed a growing share of Brazil’s federal budget. The government estimated it would spend 70.7 billion reais ($13.2 billion) next year on judicial decisions where it can no longer appeal. The figure does not include small-value claims, which historically amount to around 30 billion reais annually.

The combined amount of over 100 billion reais represents a sharp increase from 37.3 billion reais in 2015. It is equivalent to about 1% of gross domestic product, or 15% more than the government expects to spend on unemployment insurance and wage bonuses to low-income workers next year.

AGU did not provide a reason for Brazil’s rising court costs…(More)”.

The Crime Data Handbook


Book edited by Laura Huey and David Buil-Gil: “Crime research has grown substantially over the past decade, with a rise in evidence-informed approaches to criminal justice, statistics-driven decision-making and predictive analytics. The fuel that has driven this growth is data – and one of its most pressing challenges is the lack of research on the use and interpretation of data sources.

This accessible, engaging book closes that gap for researchers, practitioners and students. International researchers and crime analysts discuss the strengths, perils and opportunities of the data sources and tools now available and their best use in informing sound public policy and criminal justice practice…(More)”.

Measuring the mobile body


Article by Laura Jung: “…While nation states have been collecting data on citizens for the purposes of taxation and military recruitment for centuries, its indexing, organization in databases and classification for particular governmental purposes – such as controlling the mobility of ‘undesirable’ populations – is a nineteenth-century invention. The French historian and philosopher Michel Foucault describes how, in the context of growing urbanization and industrialization, states became increasingly preoccupied with the question of ‘circulation’. Persons and goods, as well as pathogens, circulated further than they had in the early modern period. While states didn’t seek to suppress or control these movements entirely, they sought means to increase what was seen as ‘positive’ circulation and minimize ‘negative’ circulation. They deployed the novel tools of a positivist social science for this purpose: statistical approaches were used in the field of demography to track and regulate phenomena such as births, accidents, illness and deaths. The emerging managerial nation state addressed the problem of circulation by developing a very particular toolkit amassing detailed information about the population and developing standardized methods of storage and analysis.

One particularly vexing problem was the circulation of known criminals. In the nineteenth century, it was widely believed that if a person offended once, they would offend again. However, the systems available for criminal identification were woefully inadequate to the task.

As criminologist Simon Cole explains, identifying an unknown person requires a ‘truly unique body mark’. Yet before the advent of modern systems of identification, there were only two ways to do this: branding or personal recognition. While branding had been widely used in Europe and North America on convicts, prisoners and enslaved people, evolving ideas around criminality and punishment largely led to the abolition of physical marking in the early nineteenth century. The criminal record was established in its place: a written document cataloguing the convict’s name and a written description of their person, including identifying marks and scars…(More)”.

The Judicial Data Collaborative


About: “We enable collaborations between researchers, technical experts, practitioners and organisations to create a shared vocabulary, standards and protocols for open judicial data sets, shared infrastructure and resources to host and explain available judicial data.

The objective is to drive and sustain advocacy on the quality and limitations of Indian judicial data and engage the judicial data community to enable cross-learning among various projects…

Accessibility and understanding of judicial data are essential to making courts and tribunals more transparent, accountable and easy to navigate for litigants. In recent years, eCourts services and various Court and tribunals’ websites have made a large volume of data about cases available. This has expanded the window into judicial functioning and enabled more empirical research on the role of courts in the protection of citizen’s rights. Such research can also assist busy courts understand patterns of litigation and practice and can help engage across disciplines with stakeholders to improve functioning of courts.

Some pioneering initiatives in the judicial data landscape include research such as DAKSH’s database; annual India Justice Reports; and studies of court functioning during the pandemic and quality of eCourts data; open datasets including Development Data Lab’s Judicial Data Portal containing District & Taluka court cases (2010-2018) and platforms that collect them such as Justice Hub; and interactive databases such as the Vidhi JALDI Constitution Bench Pendency Project…(More)”.

Facial Recognition: Current Capabilities, Future Prospects, and Governance


A National Academies of Sciences, Engineering, and Medicine study: “Facial recognition technology is increasingly used for identity verification and identification, from aiding law enforcement investigations to identifying potential security threats at large venues. However, advances in this technology have outpaced laws and regulations, raising significant concerns related to equity, privacy, and civil liberties.

This report explores the current capabilities, future possibilities, and necessary governance for facial recognition technology. Facial Recognition Technology discusses legal, societal, and ethical implications of the technology, and recommends ways that federal agencies and others developing and deploying the technology can mitigate potential harms and enact more comprehensive safeguards…(More)”.

Predictive Policing Software Terrible At Predicting Crimes


Article by Aaron Sankin and Surya Mattu: “A software company sold a New Jersey police department an algorithm that was right less than 1% of the time

Crime predictions generated for the police department in Plainfield, New Jersey, rarely lined up with reported crimes, an analysis by The Markup has found, adding new context to the debate over the efficacy of crime prediction software.

Geolitica, known as PredPol until a 2021 rebrand, produces software that ingests data from crime incident reports and produces daily predictions on where and when crimes are most likely to occur.

We examined 23,631 predictions generated by Geolitica between Feb. 25 to Dec. 18, 2018 for the Plainfield Police Department (PD). Each prediction we analyzed from the company’s algorithm indicated that one type of crime was likely to occur in a location not patrolled by Plainfield PD. In the end, the success rate was less than half a percent. Fewer than 100 of the predictions lined up with a crime in the predicted category, that was also later reported to police.

Diving deeper, we looked at predictions specifically for robberies or aggravated assaults that were likely to occur in Plainfield and found a similarly low success rate: 0.6 percent. The pattern was even worse when we looked at burglary predictions, which had a success rate of 0.1 percent.

“Why did we get PredPol? I guess we wanted to be more effective when it came to reducing crime. And having a prediction where we should be would help us to do that. I don’t know that it did that,” said Captain David Guarino of the Plainfield PD. “I don’t believe we really used it that often, if at all. That’s why we ended up getting rid of it.”…(More)’.

The GPTJudge: Justice in a Generative AI World


Paper by Grossman, Maura and Grimm, Paul and Brown, Dan and Xu, Molly: “Generative AI (“GenAI”) systems such as ChatGPT recently have developed to the point where they are capable of producing computer-generated text and images that are difficult to differentiate from human-generated text and images. Similarly, evidentiary materials such as documents, videos and audio recordings that are AI-generated are becoming increasingly difficult to differentiate from those that are not AI-generated. These technological advancements present significant challenges to parties, their counsel, and the courts in determining whether evidence is authentic or fake. Moreover, the explosive proliferation and use of GenAI applications raises concerns about whether litigation costs will dramatically increase as parties are forced to hire forensic experts to address AI- generated evidence, the ability of juries to discern authentic from fake evidence, and whether GenAI will overwhelm the courts with AI-generated lawsuits, whether vexatious or otherwise. GenAI systems have the potential to challenge existing substantive intellectual property (“IP”) law by producing content that is machine, not human, generated, but that also relies on human-generated content in potentially infringing ways. Finally, GenAI threatens to alter the way in which lawyers litigate and judges decide cases.

This article discusses these issues, and offers a comprehensive, yet understandable, explanation of what GenAI is and how it functions. It explores evidentiary issues that must be addressed by the bench and bar to determine whether actual or asserted (i.e., deepfake) GenAI output should be admitted as evidence in civil and criminal trials. Importantly, it offers practical, step-by- step recommendations for courts and attorneys to follow in meeting the evidentiary challenges posed by GenAI. Finally, it highlights additional impacts that GenAI evidence may have on the development of substantive IP law, and its potential impact on what the future may hold for litigating cases in a GenAI world…(More)”.

Essential Elements and Ethical Principles for Trustworthy Artificial Intelligence Adoption in Courts


Paper by Carlos E. Jimenez-Gomez and Jesus Cano Carrillo: “Tasks in courts have rapidly evolved from manual to digital work. In these innovation processes, theory and practice have demonstrated that adopting technology per se is not the right path. Innovation in courts requires specific plans for digital transformation, including analysis, programmatic changes, or skills. Artificial Intelligence (AI) is not an exception.
The use of AI in courts is not futuristic. From efficiency to decision-making support, AI-based tools are already being used by U.S. courts. To cite some examples, AI tools allow the discovery of divergences, disparities, and dissonances in jurisdictional activity. At a higher level, AI helps improve internal organization. AI helps with judicial decision consistency, exploiting a large judicial knowledge base in the form of big data, and it makes the judge’s work more agile with pattern and linguistic recognition in documents, identifying schemes and conceptualizations.

AI could bring considerable benefits to the judicial system. However, the risks and challenges are also
enormous, posing unique hurdles for user trust…

This article defines AI in relation to courts to understand challenges and implications and reviews AI components with a special focus on characteristics of trustworthy AI. It also examines the importance of a new policy and regulatory framework, and makes recommendations to avoid major problems…(More)”

Lawless Surveillance


Paper by Barry Friedman: “Here in the United States, policing agencies are engaging in mass collection of personal data, building a vast architecture of surveillance. License plate readers collect our location information. Mobile forensics data terminals suck in the contents of cell phones during traffic stops. CCTV maps our movements. Cheap storage means most of this is kept for long periods of time—sometimes into perpetuity. Artificial intelligence makes searching and mining the data a snap. For most of us whose data is collected, stored, and mined, there is no suspicion whatsoever of wrongdoing.

This growing network of surveillance is almost entirely unregulated. It is, in short, lawless. The Fourth Amendment touches almost none of it, either because what is captured occurs in public, and so is supposedly “knowingly exposed,” or because of doctrine that shields information collected from third parties. It is unregulated by statutes because legislative bodies—when they even know about these surveillance systems—see little profit in taking on the police.

In the face of growing concern over such surveillance, this Article argues there is a constitutional solution sitting in plain view. In virtually every other instance in which personal information is collected by the government, courts require that a sound regulatory scheme be in place before information collection occurs. The rulings on the mandatory nature of regulation are remarkably similar, no matter under which clause of the Constitution collection is challenged.

This Article excavates this enormous body of precedent and applies it to the problem of government mass data collection. It argues that before the government can engage in such surveillance, there must be a regulatory scheme in place. And by changing the default rule from allowing police to collect absent legislative prohibition, to banning collection until there is legislative action, legislatures will be compelled to act (or there will be no surveillance). The Article defines what a minimally-acceptable regulatory scheme for mass data collection must include, and shows how it can be grounded in the Constitution…(More)”.

The Low Threshold for Face Recognition in New Delhi


Article by Varsha Bansal: “Indian law enforcement is starting to place huge importance on facial recognition technology. Delhi police, looking into identifying people involved in civil unrest in northern India in the past few years, said that they would consider 80 percent accuracy and above as a “positive” match, according to documents obtained by the Internet Freedom Foundation through a public records request.

Facial recognition’s arrival in India’s capital region marks the expansion of Indian law enforcement officials using facial recognition data as evidence for potential prosecution, ringing alarm bells among privacy and civil liberties experts. There are also concerns about the 80 percent accuracy threshold, which critics say is arbitrary and far too low, given the potential consequences for those marked as a match. India’s lack of a comprehensive data protection law makes matters even more concerning.

The documents further state that even if a match is under 80 percent, it would be considered a “false positive” rather than a negative, which would make that individual “subject to due verification with other corroborative evidence.”

“This means that even though facial recognition is not giving them the result that they themselves have decided is the threshold, they will continue to investigate,” says Anushka Jain, associate policy counsel for surveillance and technology with the IFF, who filed for this information. “This could lead to harassment of the individual just because the technology is saying that they look similar to the person the police are looking for.” She added that this move by the Delhi Police could also result in harassment of people from communities that have been historically targeted by law enforcement officials…(More)”