# High-Risk AI Systems--Lie Detection Application
## 5 Threats and Countermeasures
The same applies for speech patterns, especially if the speech is influenced by regional accents or speech impediments. In order to counteract these vulnerabilities, adversarial testing input validation and sanitization are needed. For example, real-world implementations of biometric authentication systems in airport security have demonstrated the importance of adversarial testing by simulating potential attacks to ensure robustness against manipulation. For instance, security research has shown that biometric authentication systems successfully use encryption techniques to prevent unauthorized access to data. These measures enhance accuracy by identifying and filtering out invalid inputs, strengthening the system against deceptive manipulations. Another significant challenge is the lack of training datasets for high-stakes lies. High-stakes lies involve more significant emotional stress and different behavioral cues compared to low-stakes lies. While low-stakes datasets may not be directly applicable for training, they can still be utilized to identify overlapping patterns between both types of deception, thereby improving the system's classification capabilities in high-stakes scenarios. Field trials in controlled environments, such as courtroom simulations, have been used to evaluate the effectiveness of high-stakes datasets, highlighting their potential in improving system reliability. Another vulnerability that may affect the system's performance is anxiety. Some signs of nervousness or uncertainty may resemble the signs of deception, and, thus, the system may detect honest people as being deceptive. This can be reduced by analyzing multiple body reactions simultaneously and considering factors indicative of honesty. Studies conducted in police interrogation settings have illustrated how integrating multiple behavioral indicators can reduce false positives caused by anxiety, improving overall system accuracy. In this way, the system would be capable of differentiating behaviors because of anxiety from actual indicative deceptive signs by improving its ability to distinguish false positives from actual negatives. Individual habits such as fidgeting or avoiding eye contact also pose vulnerabilities, as they can be mistakenly interpreted as signs of deceit. Mitigation strategies include analyzing multiple body reactions concurrently, incorporating factors for honesty detection, and ensuring that datasets encompass a diverse range of individuals. This approach has  
Figure 3: Key challenges affecting AI-based lie detection systems in border control, showing the interrelationships between technical, legal, ethical, and operational considerations and their impact on system implementation. been validated in pilot studies within border control scenarios, where datasets enriched with diverse demographic profiles significantly improved detection rates. These security controls enable the system to recognize a greater variety of behavioral inputs, which ultimately results in an improvement in the system's overall accuracy. Cultural differences significantly influence body language and communication styles. A system not tailored to accommodate these variations may misinterpret behaviors common in certain cultures as deceptive. Additionally, relying on a single trait for lie detection is ineffective due to the complexity and individual variability of deception. Both issues can be addressed by analyzing multiple body reactions at once, allowing the system to capture a more holistic and culturally sensitive understanding of behavior. The scarcity of non-English datasets for speech patterns limits the system's global applicability and accuracy in multilingual contexts. This limitation can be alleviated by developing more non-English datasets, and by temporarily depending on factors beyond speech patterns. Multinational research collaborations have begun creating multilingual datasets, with preliminary results indicating notable improvements in speech-based lie detection for non-English speakers.