International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Intelligent Security Screening: Reducing Bias in Airport Checkpoints Dr. G Tej Varma1, S. Lalitha Jyothi1, K. Sravani2, V. Anusha3, K. Vasavi4 1Assistant Professor, Department of CSE, Sri Vasavi Engineering College(A), Pedatadepalli, Tadepalligudem-
534101
1-4CST, Sri Vasavi Engineering College(A), Pedatadepalli, Tadepalligudem-534101
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Abstract - By facilitating risk-based screening and
Homeland Security, the European Commission's H2020 FLYSEC project, and IATA/ACI's Smart Security. RBS improves security effectiveness, but it also brings up privacy and ethical issues, including possible racial, gender, and religious discrimination. If RBS is applied unfairly, it may result in financial losses, legal issues, and a decline in passenger confidence. RBS needs to be seen as impartial and fair to be accepted. With a focus on privacy issues considering GDPR regulations, this article explores the ethical ramifications of automated security decision making. It examines how machine learning can be used in security and suggests an expert system to identify possible biases. RBS is not new, but it has changed with developments in AI and machine learning, which allow security experts to more precisely identify threats and analyze large data sets. Notwithstanding its benefits, RBS still needs to address important issues like data privacy and transparency. To keep the public's trust, security decisions need to be transparent and well-supported. Additionally, RBS needs to be continuously assessed to remain impartial and effective. This includes regular updates to machine learning models and cooperation between governments, airlines, and privacy advocates. In the end, RBS has the power to transform airport security by improving its intelligence and effectiveness. Its success hinges on its ethical application, openness, and continuous improvement, guaranteeing a smoother and safer journey for travelers everywhere.
intelligent passenger assessment, automated decision-making is revolutionizing airport security. Making sure that decisions are made impartially is a major challenge in automated security screening. respecting privacy laws like the General Data Protection Regulation (GDPR). By integrating bias detection mechanisms, this system ensures that all passengers are treated fairly by identifying and mitigating discriminatory patterns in security checks. It also makes behavioral analysis and baggage tracking possible, which enhances security monitoring and anomaly detection. The project improves security measures while preserving passenger rights, data privacy, and operational efficiency by fusing AI-driven insights with decision-making. This strategy guarantees a more intelligent, impartial, and dependable airport security system for contemporary air travel. This system's frontend is implemented using HTML, CSS, and JavaScript, while the backend is developed using Java Servlets and JSP, with MySQL as the database. This technology stack ensures a seamless, scalable, and secure system for effective airport security screening. Key Words: (Automated Security Screening, Risk-Based Screening, Bias Detection, GDPR, Behavioural Analysis, Airport Security, AI in Surveillance
1.INTRODUCTION
2.LITERATURE SURVEY
Intelligent passenger assessment and risk-based screening are two ways that automated decision-making is changing airport security. Ensuring impartial decisions while abiding by privacy laws is a major challenge in this system. Such as the General Data Protection Regulation (GDPR). To solve this, the system incorporates bias detection tools that spot and lessen discriminatory trends in security checks, guaranteeing that every traveler is treated fairly. Additionally, it facilitates behavioral analysis and luggage tracking, which enhances anomaly detection and general security monitoring. This method improves operational efficiency, protects passenger rights, and strengthens security by fusing AI-driven insights with decision-making procedures. In airport security, risk-based security (RBS) is a developing idea that aims to strike a balance between passenger convenience and safety. Long lines and X-ray scans are examples of traditional procedures that can irritate passengers, but RBS concentrates on targeted examination of particular people, in line with behavior analysis initiatives such as those from the U.S. Department of
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Impact Factor value: 8.315
Selective security screening and risk-based approaches have been widely explored to enhance efficiency and fairness in airport security. Tamara Stotz [1] examined public perception regarding risk-based and randomized screenings, highlighting concerns over fairness, discrimination, and privacy while advocating for AI-driven bias detection. Similarly, Z. Zhang [3] analyzed risk-based passenger differentiation techniques to optimize security checks, reducing inconvenience for low-risk individuals. K. Michalski et al. [6] Decision-support systems, biometric advancements, and optimization techniques have played a crucial role in improving airport security operations. G. Scozzaro et al. [2] introduced a simulation-optimization-based system to efficiently allocate security resources, reducing wait times while maintaining security standards. J. Skorupski and P. Uchroński [4] applied data-driven queuing simulations to enhance cargo screening, ensuring both security and operational efficiency. A. Knol et al. [7] used cognitive agent
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ISO 9001:2008 Certified Journal
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