# High-Risk AI Systems--Lie Detection Application
## 1 Introduction  
Globalization has resulted in the increased mobility of individuals across national borders; this has created a compelling need to implement efficient and secure border control systems. While there have been significant developments in the use of artificial intelligence in border security applications, there is a gap in the existing literature regarding the combining of the technological innovations with ethical, legal, and societal considerations. This manuscript tries to fill this gap by performing an interdisciplinary analysis of AI-based lie detection systems. What the integration of artificial intelligence technology does is offer the possibility of automating and enhancing these procedures. The use of artificial intelligence technology creates the potential to automate and improve these procedures. In precise terms, AI-based lie detection systems promise to objectively evaluate the truthfulness of individuals during border screenings, particularly as the technologyadvances. For now, the artificial intelligence technology for lie detection in a border control system appears to function primarily as a supplement to existing human judgment rather than as a substitute for it. However, the human border control officer retains the right to make the final decision despite the technology offering vital insights and analysis. AI technologies have been increasingly adopted to enhance the efficiency and effectiveness of border control operations. Nevertheless, these technologies will ostensibly bring better security, smoother immigration procedures, and enhanced capacities for detecting and preventing illegal activities. Similar AI-driven technologies have been implemented in domains such as criminal investigations and fraud detection, where systems like voice-stress analysis and behavioral biometrics have shown promise but face similar challenges in terms of accuracy and bias [1]. At the same time, though, integrating AI into border control raises significant concerns regarding cybersecurity and privacy. The use of highly personal data, the probability of bias in AI algorithms, and the exposure to cyberattacks all mean that there is a demand for critical examination of these technologies [2]. The EU has taken a significant step by approving the EU AI Act, a comprehensive regulatory framework designed to ensure the trustworthy deployment of AI. This Act identifies explicitly \"high-risk\" AI systems used in critical infrastructures, such as border control, and subjects them to stringent legal requirements. This classification underscores the substantial impact these systems can have on the rights of individuals and societal values. The EU AI Act mandates strict compliance measures for high-risk AI systems, including data governance, transparency, human oversight, and resilience to cybersecurity threats. This paper explores the feasibility and the risks of implementing AI lie detection systems in border control. The main novelty of this work is threefold: first, we connect the emerging domain of artificial intelligence-based lie detection with the compliance provisions of the EU AI Act, highlighting the impact of regulatory scrutiny on system design; second, we provide a focused survey of current deep learning solutions for deception detection in high-stakes settings, in particular, in border control scenarios; and, finally, we propose a framework that incorporates the concepts of the EU AI Act and establishes a basis for future methodologies and research initiatives. This approach stresses the importance of linking steps in artificial intelligence with evolving legal and ethical frameworks so that these critical technologies will be used effectively and deployed responsibly. Artificial intelligence and its applications constitute a very active research domain, characterized by an abundance of scientific publications and technological works. This appears to be the inaugural comprehensive survey of AI-based lie detection within the framework of the EU AI Act.