THANK YOU FOR SUBSCRIBING
Government CIO Outlook | Monday, September 06, 2021
AI in predictive policing may offer several benefits, but it also raises some legal and ethical concerns.
Fremont, CA:AI offers promising applications for law enforcement and is already being employed in this field, whether it's in identifying fraud, traffic accidents, child pornography, or abnormalities in public space. Indeed, artificial intelligence (AI) makes law enforcement operations more efficient, less prone to human error and fatigue, and less expensive. AI is based on algorithms, and its growing prevalence corresponds to a culture that is becoming increasingly data-driven. The ability of AI to discover patterns is the most potential application for law enforcement, since it allows them to better forecast, foresee, and prevent crime. Predictive policing refers to the ability to foresee crime before it occurs. The application of AI in predictive policing has sparked debate, as it raises significant ethical and legal implications.
Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.
In predictive policing, AI algorithms are used to recognize and filter through enormous volumes of historical data on criminal behavior to identify people or places in danger. Risk or threat assessment are terms used to describe such procedures. While these algorithms are typically well-intentioned, the historical data that feeds them presents serious difficulties. First, the data could be skewed by human error: law enforcement officers could input it inaccurately or neglect it, especially since criminal data is often fragmentary and unreliable, distorting the research. Certain places and criminal groups may be over-represented in the statistics, making it incomplete and skewed. It could also stem from eras when the police engaged in discriminatory practices against specific communities, categorizing certain neighborhoods as 'high danger' arbitrarily or wrongly. These latent biases in past data sets have far-reaching implications for today's targeted communities. As a result, AI in predictive policing has been linked to racial profiling and can worsen biased analyses.
Furthermore, the data frequently concentrates on street crime, such as theft or drug trafficking, crimes that are frequently linked to specific demographic groups and neighborhoods. White-collar crimes like money laundering, corporate fraud, and corruption receive less attention. Other data, such as domestic violence statistics, is largely ignored. Furthermore, the lack of transparency in AI's operational and decision-making process is a major area of concern.
More in News