# High-Risk AI Systems--Lie Detection Application
## 5 Threats and Countermeasures  
While AI-driven lie detection technologies have certainly made some promising progress within the challenging realm of reading micro-expressions, vocal patterns, and other behavioral biometrics, it needs to be pointed out that there are a lot of big challenges standing in the way for these potentially great systems, and these are likely to seriously undermine their overall effectiveness and reliability in real-world applications. There might also be technical constraints that could undermine the accuracy and performance of these highly complex systems. Beyond the technical and practical challenges that are intrinsic in the deployment of AI-powered lie detection systems, there are substantial ethical, legal, and societal concerns that must be addressed. Problems of privacy infringement, the need for consent, the potential biases within AI algorithms, and the grave consequences of accusing innocent people all demand a comprehensive and prudent analysis of the moral implications such technologies bring forth. Moreover, legal challenges will inevitably arise concerning the admissibility of evidence derived from an AI system in a court of law, together with the strict observance of the data protection regulations in place to ensure the rights and privacy of individuals are well protected. This section provides an in-depth look into the various and intricate challenges and considerations that surround the topic at hand. It takes a comprehensive look at the current countermeasures to be found in response to lie detection technologies and explores the important ethical, legal, and societal implications of the application and use of such technologies. In so doing, we hope to provide a well-rounded understanding of the underlying complexities and nuances associated with both the advancement and the practical implementation of artificial intelligence lie detection systems. The subsequent table presents the AI threats specific to AI lie detection systems, as well as the vulnerabilities and security controls related to them. Before the Table 2, Figure 3 highlights significant challenges faced by AI-based lie detection systems, categorized into technical, legal, ethical, and operational. Technical challenges stem from system accuracy and data quality issues, while legal challenges arise from compliance with the EU AI Act and GDPR. Ethical considerations involve privacy and fairness, and operational challenges focus on training and implementation. These interconnected challenges, illustrated with dashed lines in Figure 3, reveal the complexity of deploying such technologies in real-world applications. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **iBorderCtrl** & **AVATR** & **Canadian Project** \\ \hline Strengths & Reduced Boarder Crossing & Automated Screening, Consistency & Speed and Efficiency in Screening Processes \\ \hline Weaknesses & High False Positives, Cultural & Low Accuracy (45–60\%), & Potential Algorithmic Bias, Privacy Risks \\ \hline Deployment Stage & Pilot Tests in EU (Hungary, Latvia, Greece) & Field Trials in Progress & Pilot Programs and Stakeholder Engagement \\ \hline Ethical Concerns & Privacy, Algorithmic Bias, Transparency & Algorithmic Bias, Privacy Issues & Transparency, Accountability, Data Security \\ \hline Key Innovations & Real-Time & Continuous Learning for Enhanced Risk Assessment & Integration of Diverse AI Tools for Comprehensive Screening \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{table}
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There exist numerous vulnerabilities of lie detection systems related to adversarial examples and evasion techniques that could subvert their accuracy and reliability [66]. One of the important vulnerabilities is the misclassification of facial expressions; the system might not correctly capture the facial clues because of subtle expressions or differences in how individuals move their faces.