Tag Archives: tackling crime

Facial recognition systems: ready for prime time?

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by Scott Faulds

Across the UK, it is estimated that there are 1.85 million CCTV cameras, approximately one camera for every 36 people.  From shopping centres to railway stations, CCTV cameras have become a normal part of modern life and modern policing, with research from the College of Policing indicating that CCTV modestly reduces overall crime. Currently, most of the cameras utilised within the CCTV system are passive; they act as a deterrent or provide evidence of an individual’s past location or of a crime committed.

However, advances in artificial intelligence have allowed for the development of facial recognition systems which could enable CCTV cameras to proactively identify suspects or active crime in real-time. Currently, the use of facial recognition systems in limited pilots has received a mixed reaction, with the Metropolitan Police arguing that it is their duty to use new technologies to keep people safe. But privacy campaigners argue that the technology possesses a serious threat to civil liberties and are concerned that facial recognition systems contain gender and racial bias.

How does it work?

Facial recognition systems operate in a similar way to how humans recognise faces, through identifying familiar facial characteristics, but on a much larger and data driven way. Whilst there are a variety of different types of facial recognition system, the basic steps are as follows:

An image of a face is captured either within a photograph, video or live footage. The face can be within a crowd and does not necessarily have to be directly facing a camera.

Facial recognition software biometrically scans the face and converts unique facial characteristics (distance between your eyes, distance from forehead to chin etc) into a mathematical formula known as a facial signature.

The facial signature can then be compared to faces stored within a database (such as a police watchlist) or faces previously flagged by the system.

The system then determines if it believes it has identified a match; in most systems the level of confidence required before the system flags a match can be altered.

Facial recognition and the police

Over the past twelve months, the Metropolitan Police and South Wales Police have both operated pilots of facial recognition systems, designed to identify individuals wanted for serious and violent offences. These pilots involved the placement of facial recognition cameras in central areas, such as Westfield Shopping Centre, where large crowds’ faces were scanned and compared to a police watch-list. If the system flags a match, police officers would then ask the potential match to confirm their identify and if the match was correct, they would be detained. Police forces have argued that the public broadly support the deployment of facial recognition and believe that the right balance has been found between keeping the public safe and protecting individual privacy.

The impact of the deployment of facial recognition by the police has been compared by some to the introduction of fingerprint identification. However, it is difficult to determine how successful these pilots have been, as there has been a discrepancy regarding the reporting of the accuracy of these facial recognition systems. According to the Metropolitan Police, 70% of wanted suspects would be identified walking past facial recognition cameras, whilst only one in 1,000 people would generate a false alert, an error rate of 0.1%.  Conversely, independent analysis commissioned by the Metropolitan Police, has found that only eight out of 42 matches were verified as correct, an error rate of 81%.

The massive discrepancy in error rates can be explained by the way in which you asses the accuracy of a facial recognition system. The Metropolitan Police measure accuracy by comparing successful and unsuccessful matches with the total number of faces scanned by the facial recognition system. Independent researchers, on the other hand, asses the accuracy of the flags generated by the facial recognition system. Therefore, it is unclear as to how accurate facial recognition truly is, nevertheless, the Metropolitan Police have now begun to use live facial recognition cameras operationally.

Privacy and bias

Civil liberties groups, such as Liberty and Big Brother Watch, have a raised a variety of concerns regarding the police’s use of facial recognition. These groups argue that the deployment of facial recognition systems presents a clear threat to individual privacy and privacy as a social norm. Although facial recognition systems used by the police are designed to flag those on watch-lists, every single person that comes into the range of a camera will automatically have their face biometrically scanned. In particular, privacy groups have raised concerns about the use of facial recognition systems during political protests, arguing that their use may constitute a threat to the right to freedom of expression and may even represent a breach of human rights law. 

Additionally, concerns have been raised regarding racial and gender bias that have been found to be prevalent in facial recognition systems across the world. A recent evaluative study conducted by the US Government’s National Institute of Standards and Technology on 189 facial recognition algorithms has found that most algorithms exhibit “demographic differentials”. This means that a facial recognition system’s ability to match two images of the same person varies depending on demographic group. This study found that facial recognition systems were less effective at identifying BAME and female faces, this means that these groups are statistically more likely to be falsely flagged and potentially questioned by the police.

Final thoughts

From DNA to fingerprint identification, the police are constantly looking for new and innovative ways to help keep the public safe. In theory, the use of facial recognition is no different, the police argue that the ability to quickly identify a person of interest will make the public safer. However, unlike previous advancements, the effectiveness of facial recognition is largely unproven.

Civil liberties groups are increasingly concerned that facial recognition systems may infringe on the right to privacy and worry that their use will turn the public into walking biometric ID cards. Furthermore, research has indicated that the vast majority of facial recognition systems feature racial and gender bias, this could lead to women and BAME individuals experiencing repeated contact with the police due to false matches.

In summary, facial recognition systems provide the police with a new tool to help keep the public safe. However, in order to be effective and gain the trust of the public, it will be vital for the police to set out the safeguards put in place to prevent privacy violations and the steps taken to ensure that the systems do not feature racial and gender bias.  

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Smart Chicago: how smart city initiatives are helping meet urban challenges

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Outside a Chicago theatre, with a huge 'Chicago' sign outside

By Steven McGinty

Home to former President Barack Obama, sporting giants the Chicago Bulls, and the culinary delicacy deep dish pizza, Chicago is one of the most famous cities in the world. Less well known is Chicago’s ambition to become the most data-driven city in the world.

A late convert to the smart city agenda, Chicago was lagging behind local rivals New York and Boston, and international leaders Barcelona, Amsterdam, and Singapore.

But in 2011, Chicago’s new Mayor Rahm Emanuel outlined the important role technology needed to play, if the city was to address its main challenges.

Laying the groundwork – open data and tech plan

In 2012, Mayor Rahm Emanuel issued an executive order establishing the city’s open data policy. The order was designed to increase transparency and accountability in the city, and to empower citizens to participate in government, solve social problems, and promote economic growth. It required that every city agency would contribute data to it and established reporting requirements to ensure agencies were held accountable.

Chicago’s open data portal has nearly 600 datasets, which is more than double the number in 2011. The city works closely with civic hacker group Open Chicago, an organisation which runs hackathons (collaborations between developers and businesses using open data to find solutions to city problems).

In 2013, the City of Chicago Technology Plan was released. This brought together 28 of the city’s technology initiatives into one policy roadmap, setting them out within five broad strategic areas:

  • Establishing next-generation infrastructure
  • Creating smart communities
  • Ensuring efficient, effective, and open government
  • Working with innovators to develop solutions to city challenges
  • Encouraging Chicago’s technology sector

 Array of Things

The Array of Things is an ambitious programme to install 500 sensors throughout the city of Chicago. Described by the project team as a ‘fitness tracker for the city’, the sensors will collect real-time data on air quality, noise levels, temperature, light, pedestrian and vehicle traffic, and the water levels on streets and gutters. The data gathered will be made publicly available via the city’s website, and will provide a vital resource for the researchers, developers, policymakers, and citizens trying to address city challenges.

This new initiative is a major project for the city, but as Brenna Berman, Chicago’s chief information officer, explains:

“If we’re successful, this data and the applications and tools that will grow out of it will be embedded in the lives of residents, and the way the city builds new services and policies”

Potential applications for the city’s data could include providing citizens with information on the healthiest and unhealthiest walking times and routes through the city, as well as the areas likely to be impacted by urban flooding.

The project is led by the Urban Center for Computation and Data of the Computation Institute – a joint initiative of Argonne National Laboratory and the University of Chicago. However, a range of partners are involved in the project, including several universities, the City of Chicago – who provide an important governance role – and technology firms, such as Product Development Technologies, the company who built the ‘enclosures’ which protect the sensors from environmental conditions.

A series of community meetings was held to introduce the Array of Things concept to the community and to consult on the city’s governance and privacy policy. This engagement ranged from holding public meetings in community libraries to providing online forms, where citizens could provide feedback anonymously.

In addition, the Urban Center for Computation and Data and the School of the Art Institute of Chicago ran a workshop entitled the “Lane of Things”, which introduced high school students to sensor technology. The workshop is part of the Array of Things education programme, which aims to use sensor technology to teach students about subjects such as programming and data science. For eight weeks, the students were given the opportunity to design and build their own sensing devices and implement them in the school environment, collecting information such as dust levels from nearby construction and the dynamics of hallway traffic.

The Array of Things project is funded by a $3.1 million National Science Foundation grant and is expected to be complete by 2018.

Mapping Subterranean Chicago

The City of Chicago is working with local technology firm, City Digital, to produce a 3D map of the underground infrastructure, such as water pipes, fibre optic lines, and gas pipes. The project will involve engineering and utility workers taking digital pictures as they open up the streets and sidewalks of Chicago. These images will then be scanned into City Digital’s underground infrastructure mapping (UIM) platform, and key data points will be extracted from the image, such as width and height of pipes, with the data being layered on a digital map of Chicago.

According to Brenna Berman:

“By improving the accuracy of underground infrastructure information, the platform will prevent inefficient and delayed construction projects, accidents, and interruptions of services to citizens.”

Although still at the pilot stage, the technology has been used on one construction site and an updated version is expected to be used on a larger site in Chicago’s River North neighbourhood. Once proven, the city plans to charge local construction and utility firms to access the data, generating income whilst reducing the costs of construction and improving worker safety.

ShotSpotter

In January, Mayor Rahm Emanuel and Chicago Police Department commanders announced the expansion of ShotSpotter – a system which uses sensors to capture audio of gunfire and alert police officers to its exact location. The expansion will take place in the Englewood and Harrison neighbourhoods, two of the city’s highest crime areas, and should allow police officers to respond to incidents more rapidly.

Chicago Police Superintendent Eddie Johnson highlights that although crime and violence presents a complex problem for the city, the technology has resulted in Englewood going “eight straight days without a shooting incident”, the longest period in three years.

ShotSpotter will also be integrated into the city’s predictive analytics tools, which are used to assess how likely individuals are to become victims of gun crime, based on factors such as the number of times they have been arrested with individuals who have become gun crime victims.

Final thoughts

Since 2011, Chicago has been attempting to transform itself into a leading smart city. Although it’s difficult to compare Chicago with early adopters such as Barcelona, the city has clearly introduced a number of innovative projects and is making progress on their smart cities journey.

In particular, the ambitious Array of Things project will have many cities watching to see if understanding the dynamics of city life can help to solve urban challenges.

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