Policing by Algorithm: Ethical Considerations of Big Data, Predictive Analytics, and Cloud Computing in Law Enforcement

Abstract

In law enforcement, predictive analytics techniques are increasingly used to improve public safety and operational efficiency. These methods rely on extensive data sources, so organizations must evaluate their data acquisition strategies. Agencies must determine if they collect the necessary data in suitable formats to apply predictive analytics. If there is a need for more data availability, the agency must consider establishing better records management and data collection systems. This proactive approach will enable the future implementation of predictive methodologies.

Furthermore, regression analysis and machine learning algorithms are essential tools for identifying areas with high crime rates and determining factors that strongly correlate with these areas. However, the ethical implications of using Big Data, predictive analytics, and cloud computing in law enforcement are significant. Privacy concerns, potential biases in algorithmic decision-making, and the security of cloud-stored data must be carefully considered to ensure that these technological advancements serve the public interest without violating individual rights. This abstract emphasizes the need for ethical vigilance as law enforcement agencies navigate the complex intersection of technology and social responsibility of ethical considerations of Big Data, Predictive Analytics, and Cloud Computing in Law Enforcement.