Predictive Policing

Friday, February 26, 2021


On the whole, predictive policing is a new concept in India. However, Indian police forces have gradually started taking interest in crime analytics using big data that involves the storage and analysis of volume of data in near-real time. This is aimed at predicting and inferencing patterns and trends related to human interactions and behaviours. A number of law enforcement agencies in the country have already started developing access to mature big data storage platforms such as Hadoop, NoSQL, etc. providing them an opportunity for storingthe structured digital content and unstructured data within the same platform and also providing them an option to analyse the incoming real time data to understand crime patterns within their jurisdictions.


Using predictive policing tools and technologies in a democratic society being governed by the rule of law, it is extremely important to respect the fundamental rights of individuals. Thus, while using analytics tools,challenges that are intrinsic to predictive approaches being followed by the law enforcement agencies should be avoided. One of the most important areas under consideration is the danger of an erosion of privacy and other fundamental rights and democratic principles like the presumption of innocence and the prohibition of penalties without a law. The past decade has witnessed quite a steep rise in the crime rates in India. As per the reports by National Crime Records Bureau, cognizable crimes have witnessed the largest increase of approximately 63%. Departmentalized investigations are not capable of providing a holistic picture to aid the law enforcement agencies. To counter and to effectively handle the critical law and order situations, a holistic analysis of different facets of the information is necessary.

A. Policy

    1. Inadequate safeguards to prevent misuse:     

             Predictive policing involves a precautionary response to the threat of crime on a day-to-day basis. This Preventive measure also raises concerns about inconveniencing and infringing on the rights of innocent people. The Code of Criminal Procedure in India currently provides a provision to arrest upon suspicion 13. Thus, any abuse of predictive policing system could lead to groundless arrests and internment without a suitable cause.

   2. Privacy:

              Use of data to determine hotspots or heat maps may not be a privacy concern, but use of data to identify likely individual offenders poses a privacy issue 14. Analysis of any personal information may attract the attention of public and institutions. People are concerned with the use of their personal information and most of them may not want to reveal about their behaviour.

    3. Amplification:

               Data driven decision-making systems are prone to amplifying the prevailing inequities in knowledge. Any Intervention to rectify the information conjointly feeds into the information in predictive policing that informs  decisions. The predictive technologies facea drag of discrimination that is an institutional bias (there are implicit biases in data).15

  4. Inscrutability of algorithms:

            Predictive policing algorithm in many circumstances are inscrutable to the courts. Therefore, current constitutional laws that are meant to prevent discrimination are not effective.

B. Technology

   5. Data ideology:

              Predictive policing involves over-reliance on data and it ignores many other factors. For instance, areas pointed out by heat maps or hotspot analysis are the areas considered for police patrolling by ignoring the other areas.

   6. Opacity of predictive:

              Models Predictive models are governed by what algorithm is written. It essentially draws attention to the data being used, assumptions being considered and the kind of contextual questions asked by algorithm as being entirely opaque.

   7. Security:

             Security of the data which will be used for performing analysis and storing the reports after analytics on the premise of an institution is a big concern. Need for good infrastructure facility for data safety and security should be considered.

   8. Data capture and storage:

Data can be captured from various sources at a high speed and to store the captured data is a challenge in itself. Data sources can be various social media platforms, cell phones, weather forecast reports, websites and other government agencies like Unique Identification Authority of India (UIDAI),Crime and Criminal Tracking Network and Systems (CCTNS) and National Crime Records Bureau (NCRB) after following the due data security and confidentiality procedures.

   9. Over-reliance on technology:

It is a tendency to believe that new technology will solve old problems. However, technology is just a tool to achieve the means. Predictive policing systems are in a position to analyse the information, but it is the responsibility of people using these systems to interpret the output in a method that's fair and just.

  10. Cybercrime:

Criminal innovation is another major challenge to be addressed. The data harnessed by law enforcement agencies for predictive policing to prevent and disrupt criminal activity is also valuable to criminals as it enables criminals to commit more sophisticated cyber-enabled crimes. Thus, it is important to protect such data from cyber-attacks.

Way Forward

As we have seen in this discussion paper, predictive policing promises to be a game changing concept.It is understood that the application of analytical and quantitative approaches will continue to be an important part of police activities. While it is predictive in nature, the effort involves crunching data of past crimes to forecast and thus, in essence it is largely reactionary policing with a proactive approach.Predictive policing begins with data analysis, so it is important that the Indian law enforcement agencies understand the data and goal of the analysis.

It is extremely important to obtain the concurrence of user agencies and to understand the context in which the tools are supposed to be used. In addition, the agencies are required to work closely with analysts to ensure that their findings are tactically useful and able to reduce the rates of crimes prevalent in the society. Going forward,Indian police administrators must deal with the proper scope of data collection, retention & use and be able to explain to the public how details being used to enhance public safety.

As evident, at the center of predictive policing is data: crime data, FIR data, personal data, gang data,social data, associational data, locational data, environmental data, social media data, behavioural data, consumer transactions data, personal communications, surveillance sources data and a growing web of sensors. The use of big data in world of law enforcement is still largely in its early stages but offers more incriminating bits of data to use and study. With the advent of technologies, law enforcement agencies can resort to finding suspicious activity from the digital trail that is left behind by the criminal.

One of the illustrations of the usage of predictive policing in future can be the reading machines that may get into the main stream by mid-2040s. While this tech will have profound ramifications on how courts operate and how innocence and guilt are proven, it will have an equally insightful impact on how we forecast crime.

Consider a scenario where by the late 2040s, laws are passed by governments around the world stipulating the need of handing over the contents of their online profiles, have their psychological profiles assessed by psychologists and their thought profiles documented by the thought reading machines.

Once all existing and new prisoners have been assessed, their digital, psychological, thought,demographic and criminal records will be shared with the national criminal investigation supercomputer. The thought profiles of millions of prisoners will be analysed and statistically modelled against their public metadata with the goal of isolating a collection of detailed criminal profile types. In other words, the computer will create a series of archetypes that possess a certain set of attributes that predict a certain level of criminal inclination. These criminal profiles will then be compared against the profiles of every citizen in the state.

Another illustrative application of predictive policing can be its usage with the UAV technology.With the advancements in UAV technology, many sensors that are essential to monitor and help the law enforcement agencies can be built-in various applications. Data from these various sources can then be transferred on areal-time basis to computers capable of performing complex calculations.

As the processing of data becomes efficient and with even more refined predictive algorithms, machines can autonomously drive the UAVs remotely to monitor the crime sensitive areas.This step forward in the future would enable the law enforcement agencies to reach places quickly and thus assist them in becoming omnipresent towards protecting the cities. If the cameras on UAVs are enabled with facial recognition and retina scanner, then real-time identification of individuals captured on any camera can simply help map and track the missing persons,fugitive and suspects. If the drone hovering above is able to record any incident, artificial intelligence/ deep learning shall be able to calculate the distance from nearest police station or patrolling vehicle and provide a first-hand information about the incident. Alerting the police department is just a small step, but artificial intelligence/ deep learning is capable of guiding the police patrols to the crime sites with interactive conversations.

The potential of predictive policing in Indian law enforcement is indisputably real and so is the fear of invading into the privacy of community. The ever-increasing law and order rhetoric can also lead to surveillance overreach. Indian police supervisors, advocates,communities and the corresponding governments are required to address these concerns before venturing into any large-scale implementation of such technologies.

It is critically important to analyse the applications of predictive policing across the multi-faceted procedures being followed within the criminal justice system. In fact, it has been assessed that in the different stages of a criminal procedure, from starting an investigation to gathering evidence, followed by arrest, trial, conviction and sentencing, as the individual gets subjected to serious sanctions and punitive actions by the law enforcement agencies, the system is able to obtain a higher standard of certainty about the crimes committed by the individual through predictive policing that also helps in legitimizing the particular action by agencies.

Given that the nature of predictive evidence is probability based, it can be inferred that arrest warrants or trials, on their own can have a tangible impact. Predictive policing under the current scenario has been designed to calculate the risk of future crime occurring based on statistical analysis of past crime data. It isalso important to deploy need-based and customized applications as per the unique requirements of the agencies.

Smaller agencies may not need expensive software and agencies of any size should compare open-source alternatives to commercial products. Larger agencies will want to consider more sophisticated systems. However, the key for agencies of all sizes is to think of the tools as providing situational awareness rather than crystal balls. The systems should help agencies understand the where, when and who of crime and identify the specific problems driving that criminal activity; this information will help support interventions to address these problems and reduce crime.

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