Imagine being able to detect crime hotspots in your city, use facial recognition software to match camera footage from a robbery with a database of images, or even predict the likelihood of a crime (including time and location) before it occurs. While this might sound like science fiction (Minority Report, which came out 20 years ago, of course comes to mind), these are just a few current examples of how Big Data and AI are currently being used to fight crime. One of the first uses of these technologies for crime prevention was within the Los Angeles Police Department in 2010, after they discovered that crime followed similar patterns, much like the aftershocks of an earthquake. Based on this observation, the LAPD developed a mathematical model using crime data to create an algorithm that was able to uncover patterns in the occurrence of crimes. Today, police departments around the world continue to do this, utilizing much more precise and accurate tools, (predpol, shotspotter, IBM i2 Coplink, Microsoft Azure Data Lake and Watson Analytics) that learn from large datasets, including police records, surveillance cameras and other sources to predict crime before it happens. These technologies have not only proven effective in predicting property crime, but also in preventing terrorist attacks, and tracking down sex offenders and financial criminals. Some results have been impressive, as in the UK, where digital crime maps are considered to be 10 times more effective at predicting crime than police officers.  In a recently published book on the subject, there is a summary of the major ways in which Machine Learning is (and will be) used to understand, predict, and perhaps even prevent crime. However, critics of these approaches such as Catherine O’Neil, in her 2016 book “Weapons of Math Destruction”, have raised compelling concerns.

Replicating Discriminatory Patterns?

You may wonder, with such promise and potential for Big Data and AI to revolutionize criminal justice, what’s the catch?  The answer lies in the various ethical implications that these technologies present, including the potential replication of discrimination. One of the aspects that causes the most concern is the risk that these predictive algorithmic systems will replicate racially-biased outcomes, as they are trained on historic crime data (which tends to be biased). This could, in turn, cause police officers to congregate in certain “high-crime” areas, leading to more stigmatization (and policing) of the populations living there. This issue gets even more complex when it comes to facial recognition. According to the National Association for the Advancement of Colored People (NAACP), in the USA, Black people are 5 times more likely to be stopped by the police than White people. Additionally, 56% of the incarcerated population are either Black or Latino. These statistics matter because it means that Black and Latino individuals are far more likely to have mugshots and other information stored in police databases, which are then used for cross-referencing with facial recognition data. One frightening consequence of this imbalance includes misidentification, which can lead to wrongful arrest. According to the New York Times, this exact scenario occurred in 2020 when three Black men were wrongfully arrested after being mistakenly identified by facial recognition software.

Privacy Concerns 

In addition to bias replication, facial recognition software also has privacy implications. Companies like Clearview AI, which helps law enforcement agencies with photo-matching, have recently come under scrutiny for their lack of transparency and other security flaws. They operate by collecting millions of photos taken from social media and other internet sources, without users’ consent, and selling them to law enforcement agencies. Despite the common argument that someone who has “nothing to hide” shouldn’t mind sharing their data for the “greater good”, having personal data at the disposal of companies or police departments can have serious repercussions, including (but not limited to) wrongful identification, unlawful surveillance, and possible security breaches (which happened to Clearview AI in 2020) that can lead to identity theft. These privacy concerns become even more serious under authoritative regimes, where activists and academics have linked surveillance tools to spyware programs meant to track and target those who oppose the government. An investigation conducted by The Wall Street Journal discovered that Ugandan intelligence officials used spyware to intercept communications from opposition leader Bobi Wine, providing a real-world example of this kind of repression. Taken together, despite the promises of Big Data and AI to fight crime, the use of these technologies must be carefully evaluated and regulated to protect citizens’ physical security and privacy. To achieve this, a series of measures, such as the regulation of surveillance and AI technologies, ethical supervision boards, continuous algorithmic revision, and privacy protection laws must be put into place to ensure that these tools are applied as fairly and ethically as possible.  

Join us in the edition of Links We Like as we explore the applications and implications of using Big Data and AI to predict, measure, and fight crime.