Data Policing and Compliance
Predictive analytics and big data analysis are changing business practices all over the world. Government departments have also been adopting the technology to ensure more efficient and effective processes. One such department is the police department that uses predictive data policing apps to identifyprospective criminals or victims to reducethe overall crime rates.
Data policing refers to the use of predictive and data analytics tools for investigative purposes. The technology is being adopted in most of the developed world due to resource pressures. The adoption of data policies is expected to result in an efficient utilization of resources to fightcrime.
Data Policing Tools: An Overview
Data policing tools can help the police become more proactive in combating criminals. Using predictive and big data analytics tools allow police officers to visualize crimes in a particular region and target specific blocks and gangs.
These apps correlate predictive risk factors with criminal activities. Using the data policing app, the police can map the associations, communications, and suspicious movements of criminals. The app will help identifypatterns from a large amount of data. It aggregates data and reveals hidden patterns that are not possible to identify when going through individual data sources.
The information provided by the data policing app is valuable for police enforcement personnel in different ways.
Big data and predictive analytics can help police officers to become more proactive in fighting crime. They can understand suspected targets and prevent criminals from executing the plans. This contrasts with the traditional ‘reactive’ policing method where the police officers react to information from phone calls or patrols.
Compliance Issues with Data Policing Apps
Adopting data policing technologies can help the police department to prioritize its resources. The tools can be twice as effective in combating crime as compared to traditional policing methods. But, the adoption of any technology that involves using personal information raises certain regulatory and ethical concerns.
Understand Data Protection Regulations
The EU General Data Protection Regulations (GDPR) and the US Federal Trade Commission Act (FTC) prohibit the disclosure of private user information. Companies that disclose personal information can be held liable for non-compliance with data protection regulations. Understanding data protection laws is important when developing data policing applications.
Application development firms should consider whether data policing apps violate limitations regarding the use and share of personal data. The app should have a secure security algorithm that prevents malicious users from accessing information stored in the data policing database.
Consider Ethical and Legal Issues
The FTC has made specific recommendations regarding the use of data policing apps. Police departments and app developers should consider the following issues that can arise when using data policing apps.
- Does the data policing app use a representative data set?
- How accurate are the actual predictions made using the app?
- Does the use of data policing app raise fairness or ethical concerns?
- Does the app account for biases?
Human biases are a particularly important concern with data policing apps. Poorly developed artificial intelligence (AI) features of the app can result in incorrect identification of possible criminal activities. Many AIs have been shown to perform poorly when it comes to the profiling of dark-skinned people. The biases can threaten the policing of minority groups within a country.
Data policing algorithms should not incorrectly assume a particular race or religion to be a higher threat. The predictions will cause the police to aggressively target a particular group. The biases in prediction raise ethical and fair usage issues that can become the cause of a lawsuit.
Create a Policy Framework
A policy framework should be created for the use of data policing applications. The application should be developed with the bounds of policy. The policy should be clearly written that takes into consideration local regulations regarding legal and fair usage of data within the policing environment.
Police forces should not make decisions based on an automated basis. They should be allowed to manually assess the risk factors before making any decision. The task of the data policing tool should be to provide suggestions, with the police personal having the final authority of judging individuals and making decisions.
Access to Third-Party Data
Another important concern that should be addressed to stay compliant with the regulations relates to the use of third-party data. Policing apps make the best predications when they process information from different sources. But processing information from multiple sources also raises the issue ofdata privacy.
Using data from social services, health care, and local authorities can lead to significant data protection issues. The use of personal information in predictive analytics should justify the benefits. The intrusion should be in accordance with the local data protection regulations. It should be demonstrated that the use of information is in the best interests of the public to prevent crime.
For instance, Article 2 of the European Convention on Human Rights implies the obligation to implement preventive operational measures to protect the lives of individuals. The safety and security of individuals should be a priority and data policing algorithms should contribute to fulfilling this responsibility. In other words, there should be a clear reason for accessing personal information to make data policing predictions.
Transparency of Process
Data policing software should have a clear and transparent process of making predictions. The documents should clarify how the algorithm works in making distributions. The AI capability of the app that relates to the use of personal information for making predictionsmust be transparent and clearly communicated.
Developers should explain in the documentation of how the data policing tool identifies patterns and associations. It should only focus on activities that are criminal in nature to make predictions. This is important to ensure compliance with all the data protection regulations.
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