Criminal Justice and Technology

 City Surveillance Watch: Balancing Act (podcast)

"In this first episode of City Surveillance Watch, a new limited podcast series, Kate Kaye explores the inherent dichotomy of data-hungry technologies that can be considered forms of surveillance."

From Facial Recognition, to Predictive Technologies, Big Data Policing is Rife with Technical, Ethical, and Political Landmines

"In mid-2019, an investigative journalism/tech non-profit called MuckRock and Open the Government (OTG), a non-partisan advocacy group, began submitting freedom of information requests to law enforcement agencies across the United States. The goal: to smoke out details about the use of an app rumoured to offer unprecedented facial recognition capabilities to anyone with a smartphone."

2021 Cybersecurity and IT Failures Roundup

"The pandemic year just passed once again demonstrates that IT-related failures are universally unprejudiced. Companies large and small, sectors private and public, reputations stellar and scorned: none are exempt. Herewith, the failures, interruptions, crimes and other IT-related setbacks that made the news in 2020."

How China Uses AI to Identify "Suspicious" Muslims for Predictive Policing

"Chinese authorities have been using predictive software to select Muslim minorities for detention based on seemingly innocuous behaviour, according to a report by Human Rights Watch (HRW) released on Wednesday."

Getting the Future Right - Artificial Intelligence and Fundamental Rights

"Artificial intelligence (AI) already plays a role in deciding what unemployment benefits someone gets, where a burglary is likely to take place, whether someone is at risk of cancer, or who sees that catchy advertisement for low mortgage rates. Its use keeps growing, presenting seemingly endless possibilities. But we need to make sure to fully uphold fundamental rights standards when using AI. This report presents concrete examples of how companies and public administrations in the EU are using, or trying to use, AI. It focuses on four core areas - social benefits, predictive policing, health services and targeted advertising."

Artificial Intelligence and Predictive Policing: A Roadmap for Research

 Link to Report

"In this report, we present the initial findings from a three-year project to investigate the ethical implications of predictive policing and develop ethically sensitive and empirically informed best practices for both those developing these technologies and the police departments using them."

Contextual Fairness: A Legal and Policy Analysis of Algorithmic Fairness

Link to Full Text

"To date, all stakeholders are working intensively on policy design for artificial intelligence. All initiatives center around the requirement that AI algorithms should be fair. But what exactly does it mean? And how algorithmic fairness can be translated to legal and policy terms? These are the main questions that this paper aims to explore."

The Global Expansion of AI Surveillance

Link to Full Text

"Artificial intelligence (AI) technology is rapidly proliferating around the world. Startling developments keep emerging, from the onset of deepfake videos that blur the line between truth and falsehood, to advanced algorithms that can beat the best players in the world in multiplayer poker. Businesses harness AI capabilities to improve analytic processing; city officials tap AI to monitor traffic congestion and oversee smart energy metering. Yet a growing number of states are deploying advanced AI surveillance tools to monitor, track, and surveil citizens to accomplish a range of policy objectives—some lawful, others that violate human rights, and many of which fall into a murky middle ground."

Predictive Policing Poses Discrimination Risk Think Tank Warns, but AI shouldn't be Dismissed

"The use of data analytics and machine learning in policing has plenty of potential benefits, but it also presents a significant risk of unfair discrimination, security think tank Royal United Services Institute for Defence and Security Studies (RUSI) has warned.
 
A new report, Data Analytics and Algorithmic Bias in Policing, outlines the various ways that analytics and algorithms are used by police forces across the United Kingdom. 

This includes the use of facial recognition technology, mobile data extraction, social media analysis, predictive crime mapping, and individual risk assessment. The report focuses on the latter two and the risks they pose, given the predictive nature of these uses.

The study notes that if bias finds is way into these technologies, it could lead to discrimination against protected characteristics such as race, sexuality or age. This is a result of human bias in the data used to train these systems." 

Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System

Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System
"This report documents the serious shortcomings of risk assessment tools in the U.S. criminal justice system, most particularly in the context of pretrial detentions, though many of our observations also apply to their uses for other purposes such as probation and sentencing. Several jurisdictions have already passed legislation mandating the use of these tools, despite numerous deeply concerning problems and limitations. Gathering the views of the artificial intelligence and machine learning research community, PAI has outlined ten largely unfulfilled requirements that jurisdictions should weigh heavily and address before further use of risk assessment tools in the criminal justice system."

Predictive Policing is Tainted by "Dirty Data," Study Finds
"A new study from New York University School of Law and NYU's AI Now Institute concludes that predictive policing systems run the risk of exacerbating discrimination in the criminal justice system if they rely on 'dirty data.'

Law enforcement has come under scrutiny in recent years for practices resulting in disproportionate aggression toward minority suspects, causing some to ask whether technology – specifically, predictive policing software – might diminish discriminatory actions.

However, a new study from New York University School of Law and NYU's AI Now Institute concludes that predictive policing systems, in fact, run the risk of exacerbating discrimination in the criminal justice system if they rely on 'dirty data' – data created from flawed, racially biased, and sometimes unlawful practices.

The researchers illustrate this phenomenon with case study data from Chicago, New Orleans, and Arizona's Maricopa County. Their paper, 'Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice,' is available on SSRN."

Police Across the US are Training Crime-Predicting AIs on Falsified Data
"In May of 2010, prompted by a series of high-profile scandals, the mayor of New Orleans asked the US Department of Justice to investigate the city police department (NOPD). Ten months later, the DOJ offered its blistering analysis: during the period of its review from 2005 onwards, the NOPD had repeatedly violated constitutional and federal law.

It used excessive force, and disproportionately against black residents; targeted racial minorities, non-native English speakers, and LGBTQ individuals; and failed to address violence against women....

Despite the disturbing findings, the city entered a secret partnership only a year later with data-mining firm Palantir to deploy a predictive policing system. The system used historical data, including arrest records and electronic police reports, to forecast crime and help shape public safety strategies, according to company and city government materials. At no point did those materials suggest any effort to clean or amend the data to address the violations revealed by the DOJ. In all likelihood, the corrupted data was fed directly into the system, reinforcing the department’s discriminatory practices....

 But new research suggests it’s not just New Orleans that has trained these systems with 'dirty data.' In a paper released today, to be published in the NYU Law Review, researchers at the AI Now Institute, a research center that studies the social impact of artificial intelligence, found the problem to be pervasive among the jurisdictions it studied."

Link to Full Report
 

Busted by Big Data: Algorithms Could Make Cities Safer - But They Can't Protect Us From Policing's Worst Instincts 
"By combining huge tranches of data and highly sophisticated algorithms, predictive policing appears to hold out the science-fiction promise that technology could, one day, spit out 100 percent accurate prophecies concerning the location of future crimes. The latest iteration of these analytics can’t ID a killer-to-be, but it can offer insight into what areas are potential sites for crime by drawing on information in everything from historical records to live social-media posts.

The technology, however, has raised tough questions about whether hidden biases in these systems will lead to even more over-policing of racialized and lower-income communities. In such cases, the result can turn into a feedback loop: the algorithms recommend a heightened police presence in response to elevated arrest rates that can be attributed to a heightened police presence.

Andrew Ferguson, who teaches law at the University of the District of Columbia and is the author of The Rise of Big Data Policing, goes further. He says that current predictive systems use social media and other deep wells of personal information to predict whether certain offenders may commit future crimes—an Orwellian scenario. Canadian governments and civilian oversight bodies, however, have done little to establish clear policies differentiating appropriate and inappropriate uses for these technologies. It is little wonder that critics are becoming increasingly concerned that police departments fitted out with big-data systems could use them to pre-emptively target members of the public. Can we really trust crime fighting to an algorithm?"

Related:

•  AI Experts Want Government Algorithms to be Studied like Environmental Hazards
• A Pioneer in Predictive Policing is Starting Another New Project
• "Partially Generative Neural Networks for Gang Crime Classification" 

An Intelligence in our Image: The Risk of Bias and Errors in Artificial Intelligence
"Machine learning algorithms and artificial intelligence systems influence many aspects of people's lives: news articles, movies to watch, people to spend time with, access to credit, and even the investment of capital. Algorithms have been empowered to make such decisions and take actions for the sake of efficiency and speed. Despite these gains, there are concerns about the rapid automation of jobs (even such jobs as journalism and radiology). A better understanding of attitudes toward and interactions with algorithms is essential precisely because of the aura of objectivity and infallibility cultures tend to ascribe to them. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems. This report highlights the added risks and complexities inherent in the use of algorithmic decisionmaking in public policy. The report ends with a survey of approaches for combating these problems."

View the Report
 

Code-Dependent: Pros and Cons of the Algorithmic Age
"Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment."