# High-Risk AI Systems--Lie Detection Application
## 6 Conclusions  
As demonstrated, the use of AI-based lie detection is still largely nascent and underdeveloped for a broader practical application. Current technologies face significant challenges concerning precision, reliability, and ethical feasibility. A lot of work lies ahead, both in technological development and the overcoming of the ethical and legal obstacles standing in the way of their feasibility. That includes refining regulations in line with the EU AI Act by including algorithmic transparency and bias mitigation provisions. Therefore, future research should look into developing culturally diverse datasets and improving the interpretability of AI models to increase user trust in these technologies. That is, pilot programs and empirical evaluations--such as controlled simulations involving real or incentivized high-stakes scenarios--to rigorously assess performance and viability under controlled conditions, with an eye to validation of the effectiveness of the system and  
Figure 4: Three-layered proposed framework and indicative key features. concerns around its impact on society through collaboration between technologists, policymakers, and stakeholders, will ensure that the development of these technologies is done responsibly. Another key development is that of comprehensive ethical frameworks for addressing issues of privacy, consent, and potential biases. Involving ethicists and related stakeholders in the development phase will help align the technology with societal values and respect for human rights. Furthermore, there will be a need to review and develop the legal frameworks in line with technological development so as to clarify the admissible use of AI in lie detection and establish accountability mechanisms in case of misuse or inaccuracy. Notably, under the EU AI Act, border control technologies are considered high-risk AI systems. This classification underlines the high importance of strict regulatory oversight and adherence to the rule of law in the application of artificial intelligence technologies in sensitive areas like border management. Ways in which transparency of AI systems can be improved, ensuring that their decision-making processes are transparent and interpretable, may help to earn the trust of users and those affected by the technology. Collaboration among technologists, policymakers, legal experts, and the public should be encouraged to ensure that there is open dialogue, and consultations can bring forward concerns early enough to mold the creation of technologies in a socially acceptable way. Controlled pilot programs are important to test AI lie detection technologies for effectiveness and impact on society. Pilot programs should be well-monitored, and their findings should inform both the development of the technology and the creation of policies. By exploring these areas, future research and policymaking can work together to help bring about the potential benefits of artificial intelligence in deception detection while at the same time mitigating related risks. It is important that as we move forward in technological advancement, we move forward equally in ethical and legal thinking to ensure that these powerful tools are used responsibly and for the betterment of society. Our analysis couples modern approaches in lie detection using artificial intelligence with regulatory contexts; it has, however, three main limitations. First, commercial and government border control systems are often proprietary, which limits access to comprehensive performance data. Second, the rapid development of deep learning technologies may render some referenced techniques obsolete or significantly improved in short periods, thus posing challenges to their long-term feasibility. Third, collecting high-stakes deception data at border entry points raises essential ethical and legal issues, so large-scale, publicly sharable datasets are not readily available. These problems can be remedied only by collaborating with industry leaders, academic institutions, and governmental agencies.