# High-Risk AI Systems--Lie Detection Application
## 3 Current AI Technologies Used for Lie Detection
### Behavioral Biometrics  
Behavioral biometrics [28] is a relatively new and fast-advancing discipline in which artificial intelligence is used to assess a whole variety of behavioral patterns that a person displays. These patterns include specific measurable parameters such as eye movement metrics (e.g., saccade frequency, fixation duration, and blink rate), gesture frequency, and posture stability indices, which have been increasingly explored in real-world scenarios, such as wayfinding tasks, to understand and leverage their biometric potential [29]. These include aspects such as eye movement, the frequency at which any particular gestures are made, and the general posture of a person. The leading aspect under investigation is that of looking for patterns which may indicate deception. Based on continuous observation and intimate analysis of such complex patterns, artificial intelligence systems are able to discern minute departures from that which is statistically regarded as normative behavior. For instance, changes in the rate of blinking or increased micro-expressions may be possible indicators of psychological stress or deception, as advanced methods in micro-expression recognition increasingly demonstrate the potential to accurately interpret such subtle behavioral cues [30]. However, the accuracy of these identifications depends on error analysis and presentation attack detection mechanisms, such as the False Acceptance Rate (FAR) and the False Rejection Rate (FRR), which can vary according to data quality and algorithms used [31]. The implications of this innovative use of technology are many and show bright promise in several fundamental areas such as, but not limited to, security protocols, the process of authentication of users, and even different evaluations connected with mental health. Those are systems using sensors, inclusive of high-resolution cameras that capture subtle movements of the face, infrared sensors that identify heat patterns, and accelerometers or gyroscopic sensors quantifying the stability of posture and movement [32]. The sensors must be calibrated in all these cases to ensure their precision, particularly in changing environmental conditions. Calibration involves adjusting the sensor parameters to account for differences in lighting, distance, and individual physiological differences among users. The study \"Machine Learning for Cognitive Behavioral Analysis\" [33] takes an in-depth look at the myriad of applications that exist for artificial intelligence and machine learning techniques within the analysis of cognitive behaviors, specifically focusing on the detection of deception. The research reports an average accuracy rate of 85% in detecting deceptive behaviors using a combination of neural network models and support vector machines, highlighting the importance of precise measurable parameters in enhancing detection capabilities [34]. The research propounds the very important necessity of acquiring and developing a deep understanding of human behaviors through assessing interrelations existing between actions, cognitive processes, and emotional responses. Also, it reveals the meaningful role that artificial intelligence can play in recognizing and identifying any digression or abnormality that may occur in these particular aspects of human behavior. Also, it addresses how critical it is to perform regular calibration of AI models so that their accuracy is maintained for long-term periods and among different populations. On the other hand, the article \"The Utility of Behavioral Biometrics in User Authentication and Demographic Characteristic Detection\" [28] carries out an overall review of various applications of behavioral biometrics, focusing on their role in the processes related to authenticating users and identifying demographic characteristics.