Information system Integrated Border Security program

A Quantitative Assessment of AI-Driven Surveillance Solutions in U.S. Immigration Control

Abstract

This study explores the effects of AI powered surveillance solutions on operational effectiveness, privacy and user experience for U.S. immigration control. Cross Sectional Quantitative survey level of 200 border security professionals, utilized the use of AI integration to examine how it impacts their perceptions of system functionality and ethical considerations in border security contexts. Our findings suggest that the higher the level of AI integration, the more operational efficiency is experienced, according to the majority of the participants, showing how AI can reduce the complexity of workflows and improve the threat detection. The privacy concerns were quite significant among the IT and security specialists; they are in desperate need for privacy focused protocols and transparent data governance to bring the trust factor. Also, user experience was dependent on professional roles and experience levels and more experienced users indicated a higher degree of satisfaction and fewer operational issues. It concludes that designing AI systems for border security is not a matter of simply applying AI to the task but finding a balance between functionality and privacy protections and adequate training for AI systems. These insights will guide policymakers and security agencies, as they strive to leverage AI driven systems towards optimizing security outcomes in a way that also addresses ethical concerns.

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