{"id":4066,"date":"2024-09-02T12:07:27","date_gmt":"2024-09-02T10:07:27","guid":{"rendered":"https:\/\/www.rivistaeco.com\/?p=4066"},"modified":"2024-09-02T12:07:27","modified_gmt":"2024-09-02T10:07:27","slug":"minority-report-is-a-reality-preventing-crime-with-ai-is-possible","status":"publish","type":"post","link":"https:\/\/www.rivistaeco.com\/en\/2024\/09\/02\/minority-report-is-a-reality-preventing-crime-with-ai-is-possible\/","title":{"rendered":"Minority Report is a Reality: Preventing Crime with AI is Possible"},"content":{"rendered":"

Predictive policing involves the use of AI systems to predict where and when a crime will occur. It is increasingly used in Italy with promising results. However, it raises significant ethical and legal questions, starting with the dangers of discrimination against specific communities or marginalised areas.<\/span><\/i><\/p>\n

In various aspects of life, we tend to be guided by habit. Criminals often do the same, with routine behaviours inspired by their experiences, specialisations, or the belief in following an ideal strategy. Law enforcement leverages these patterns with the help of predictive policing algorithms designed to decide the best placement of patrols based on predictions about where crimes will be committed. The combination of artificial intelligence technologies and advanced data collection strategies improves the efficiency and effectiveness of police work, helping to process large amounts of data, predict criminal acts, and identify potential threats. However, it is a technology not without risks.<\/span><\/p>\n

Benefits and Limits of Predictive Policing<\/b><\/p>\n

Starting with the benefits: what are they? Predictive policing is an advanced form of hotspot control: areas that have recently experienced high crime rates are likely to experience more crimes in the future. Thus, they are subjected to increased police surveillance to deter criminal activities. AI algorithms can predict hotspots more accurately than traditional methods, but proving their effectiveness in reducing crime is more complex. For instance, police departments often adopt predictive systems during crime waves, which may subside independently of intervention. It is therefore difficult to separate the software’s effects from natural crime fluctuations.<\/span><\/p>\n

There are some studies and research that provide evidence on the effectiveness of predictive policing. For example, Anthony A. Braga, Brandon Turchan, Andrew V. Papachristos, and David M. Hureau concluded in their work that police activity in hotspots generally reduces crime. However, possible benefits are countered by limitations. The effectiveness of patrols in high-crime areas may be short-lived. Additionally, criminal activities may simply shift to nearby unsupervised areas. For example, my research shows that when banks increase security guards to protect their premises, robbers tend to target nearby unprotected branches.<\/span><\/p>\n

One crucial problem with predictive policing is the possibility of falling into forms of discrimination, especially against marginalised communities. Predictive algorithms can inadvertently perpetuate existing biases because they concentrate more resources in depressed areas with higher crime rates, thus increasing the likelihood of arrests in these neighbourhoods. It is a vicious cycle that can distort perceptions of crime distribution and unjustly target specific communities. Ensuring fairness requires distinguishing between repeat offenders and first-time offenders because algorithms that treat all crime episodes the same end up penalising those who commit a crime for the first time in a high-crime area. A better use of these tools should aim to target only repeat offenders, deterring them from committing further crimes. And when it comes to key figures in the criminal network, it can lead to a real reduction in area crimes through their influence on other criminals operating in the zone.<\/span><\/p>\n

The Italian Approach to Predictive Policing<\/b><\/p>\n

An interesting innovation in this field is the KeyCrime software tested by the Milan police headquarters. Unlike typical predictive policing tools, KeyCrime focuses on identifying and arresting repeat offenders rather than mere crime deterrence. By analysing detailed information gathered from victims’ testimonies and CCTV footage, it predicts specific crimes and targets habitual criminals. This approach and the human evaluation of predictions reduce the margin of bias and, as my research shows, lead to promising results in arresting habitual robbers by exploiting their predictable behaviours.<\/span><\/p>\n

The figures compare the predicted characteristics of robberies at commercial establishments without using any information on previous crimes committed by the criminal group with the predictions obtained by analysing the previous crimes of the same gang. For example, considering targets, the probability of a bank being targeted is only 12%, but if the previous target of the gang was a bank, the probability rises to over 80%. In other words, criminals specialise, and it is a fact that can be exploited to predict their next move. They also specialise in terms of means of transport, space, and day of the week. Artificial intelligence can detect these habits and predict the most likely future target of a criminal group.<\/span><\/p>\n

Do Criminals Specialise?<\/b><\/p>\n

\"\"<\/p>\n

Perseverance in Targets<\/b><\/p>\n

\"\"<\/p>\n

Source: Elaborations on data from the Milan Police Headquarters collected through KeyCrime.<\/span><\/p>\n

The success of KeyCrime in Milan suggests that predictive policing tools can be refined to minimise biases and improve effectiveness. Extending personalised predictive models to other types of crimes, such as thefts and violent crimes, could further improve results.<\/span><\/p>\n

The Limits of Facial Recognition<\/b><\/h3>\n

The ability of artificial intelligence algorithms to recognise patterns is also used in other areas of law enforcement, though it remains uncertain whether the benefits outweigh the costs. For example, AI-enhanced facial recognition systems are widely used to identify suspects in airports and monitor public spaces. However, the way algorithms are trained is crucial, and biases can be severe. A recent study by the US National Institute of Standards and Technology found that facial recognition technologies are less accurate on women of colour and generally tend to make more frequent errors in identifying people of colour compared to whites. This happens because the technology is trained on datasets that predominantly include whites. Therefore, it is important to collect large amounts of unbiased data.<\/span><\/p>\n

Hexagon, a rapidly growing high-tech company led by Italian engineer Paolo Guglielmini, specialises in digitising the world around us. Artificial intelligence, combined with data from various sources such as license plate readers, surveillance cameras, gunshot detection sensors, or social media, can help detect patterns and anomalies, allowing for faster responses to incidents and helping analysts identify suspects. For example, AI license plate recognition systems are widely used to track vehicles involved in criminal activities. AI-enhanced gunshot detection systems like ShotSpotter in the US analyse acoustic data to identify gunshots and determine where they occur in real-time: the faster police response can sometimes save lives.<\/span><\/p>\n

Social media itself can be a treasure trove for law enforcement. Criminals sometimes boast about their exploits on social media, and AI tools are increasingly used to monitor platforms for potential threats, such as posts indicating illegal activities. Drones equipped with AI technology are used for aerial surveillance: their real-time data over large areas allow for gathering information that ground units might not access. This improves surveillance and operational effectiveness during large events or in remote areas.<\/span><\/p>\n

AI is also used in the judiciary to assess the risk of recidivism and allow judges to make informed decisions not only in the United States but also in some European countries. Tools like the Harm Assessment Risk Tool in the UK and Compas (Correctional Offender Management Profiling for Alternative Sanctions) in the US analyse the defendant’s criminal history along with other factors to predict the likelihood of recidivism. These tools aim to improve the precision and fairness of judicial sentences, but there have been criticisms for potential discrimination against certain demographic groups. The algorithms used are legally protected and thus are a sort of “black box” by definition, not transparent. If some minorities\u2014Blacks, Hispanics, or immigrants\u2014are subject to discrimination within the judicial system, making them more likely to be arrested and convicted, it is very likely that predictive algorithms will continue to perpetuate the same biases. Whether awareness of algorithmic biases is sufficient to cancel out potential discrimination remains an open question that requires further research.<\/span><\/p>\n

Finally, there are more extreme AI systems used in autocratic regimes that even identify individuals who might decide to commit crimes in the future. China uses facial recognition and AI to monitor behaviours and detect suspicious activities. The system can track individuals over time and alert authorities if someone shows potentially criminal behaviours like loitering repeatedly in a specific area. The risk of facing legal consequences for actions that might be committed in the future closely resembles the dystopian future portrayed in the film Minority Report, in addition to being contrary to our rule of law.<\/span><\/p>\n

Improving Algorithms<\/b><\/p>\n

In conclusion, the use of AI in law enforcement ensures benefits but raises significant ethical and legal concerns. To mitigate these risks, it is essential to introduce strict measures on oversight and accountability and classify predictive algorithms based on their potential for bias. Any algorithm using potentially biased data or whose predictions are too approximate and not based on observed criminal behaviours should be handled with caution. Police forces should adopt transparent policies for the use of AI, including regular audits and public reports on its results. Involving communities and civil rights organisations can help ensure that AI tools are used while respecting individual rights.<\/span><\/p>\n

As predictive policing technologies evolve, continuous evaluation is essential to ensure their effectiveness and fairness. Research should focus on long-term impacts and potential criminal behaviour adaptations. Privacy laws must balance the invasiveness of collecting information on individuals with the benefits that can derive for society as a whole. It is already clear that different countries place very different weights on individual protection versus societal protection, even when the set of rules is the same. For example, within the European legislation GDPR (General Data Protection Regulation), Scandinavian and Dutch authorities and researchers have access to large amounts of administrative data, while in Germany and Italy, it is practically impossible to cross-reference individual data held by different institutions. As Agatha Christie observed, “Crime is terribly revealing,” and sophisticated use of predictive analysis could indeed increase the effectiveness and fairness of the fight against crime. With the right safeguards, AI can make communities safer while protecting civil liberties.<\/span><\/p>\n

Bio<\/b><\/em><\/p>\n

Giovanni Mastrobuoni is a full professor of political economy at the University of Turin and Carlo Alberto Chair and Vice-Dean at the Collegio Carlo Alberto in Turin. He is a research fellow at CEPR and the IZA-Institute of Labor Economics.<\/span><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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