# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 2 Related Works
AI regulations among the other regions of the world (U.S., China, Canada, and South Korea) reflect diverse national and state-level policies and legal traditions and differ in terms of implementation, scope, and enforcement among themselves as well as in comparison to the EU AI Act. The state/federal guidelines by the U.S. offers a more fragmented, industry-specific, and decentralized approach. Executive orders (such as on trustworthy AI), the CHIPS and Science Act, and agency guidelines are a part of federal actions. The states of Colorado and California have proposed or enacted laws for AI accountability, transparency, and anti-discrimination. Colorado has been known to focus on high-risk systems in high-impact sectors such as healthcare and recruitment [28]. The AI and Data Act (AIDA) [29] by Canada emphasizes safety, human rights, responsible AI practices, oversight, and public sector directives.This regulation also follows risk-based tiers, posing stricter rules for AI systems in high-impact sectors. China's New Generation AI Development Plan [30] is a national strategy with sector-specific regulations for AI algorithms, generative AI, and deep synthesis. The Draft AI Law (2024) would impose legal requirements on high-risk and critical AI systems. China encourages innovation but maintains strict regulatory oversight, especially for public-facing AI services. A key limitation is the interpretability of the original legal document, which is available only in the Chinese language. South Korea's Basic Act on AI Advancement and Trust [31] focuses on trust, advancement, and transparency for high-impact systems in healthcare, energy, and public services. Risk-based classification of AI systems, requirements for transparency and accountability, emphasis on ethics and human rights, and sector-specific rules are the common themes across the several regulatory frameworks. Unlike the other frameworks, the EU AI Act follows strict high-risk rules with high fines, while covering seven different trustworthiness aspects--data governance, record-keeping, transparency, human-oversight, accuracy, robustness, and cybersecurity. Research on LLM evaluation methods, such as [32], addresses key questions about what, where, and how to assess performance. Further, the work of [17] introduces the Think-Solve-Verify (TSV) framework to enhance reasoning accuracy and trustworthiness through introspective reasoning and self-consistency. Together, these studies shed light on the importance of comprehensive evaluation frameworks and interdisciplinary approaches to ensure the responsible development and deployment of LLMs. Our study, however, takes a distinct perspective from the previous works by conceptualizing trustworthiness within the context of domain-specific LLM applications. It focuses particularly on advanced cellular generations, non-cellular technologies, and a broader range of high-impact application domains extending beyond telecommunications. By examining trustworthiness within both regulatory and technological frameworks, we provide insights crucial for the responsible implementation of LLMs with ensured safety in critical infrastructures.