With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. Most of the existing related reviews explore the application of AI in blockchain as a whole but lack in-depth classification and discussion on how AI can empower the core aspects of blockchain. This paper explores the application of artificial intelligence technologies in addressing core challenges of blockchain systems, specifically in terms of scalability, security, and privacy protection. Instead of claiming a deep theoretical integration, we focus on how AI methods, such as machine learning and deep learning, have been effectively adopted to optimize blockchain consensus algorithms, improve smart contract vulnerability detection, and enhance privacy-preserving mechanisms like federated learning and differential privacy. Through comprehensive classification and discussion, this paper provides a structured overview of the current research landscape and identifies potential directions for further technical collaboration between AI and blockchain technologies.

This study confronts the ethical crisis in machine translation (MT) caused by the systematic erosion of cultural semantics and symbolic integrity. While current MT systems achieve high technical performance (e.g., 72.3 BLEU scores in WMT2022), they fail to preserve cultural-contextual nuances, with 34.8% mistranslation rates for culturally loaded terms and 73% semantic reduction for low-resource language symbols. Through deconstructive analysis of Saussure’s arbitrariness principle and Wittgenstein’s language game theory, we demonstrate how algorithmic compression of dynamic semantic networks into unidirectional referential chains perpetuates symbolic violence, exemplified by the 89% loss of Confucian ethical dimensions in translating Chinese ren (benevolence) and 82% erasure of spiritual connotations in Arabic jihad. To address these issues, we propose the Symbolic Ethics Sensitivity Assessment Model (SESAM), a tripartite framework evaluating cultural fidelity, symbolic violence reduction, and ethical risk mitigation on a 0-5 scale. Validated in Meta’s No Language Left Behind project, SESAM increases cultural metaphor retention by 23.6% while maintaining translation efficiency. By integrating Peircean semiotics with computational linguistics, we construct a Cultural Semantic Vector Space using GloVe embeddings, resolving 93% of Traditional Chinese Medicine concept mapping failures. The study further develops the Linguistic Ethics Entropy (LEE) index, benchmarked at ≤0.23 for EU AI Act compliance, as the first quantitative metric for ethical MT. Our findings reveal three advancements: 1) semiotic topology exposing 47:1 corpus imbalance-induced cultural hegemony, 2) Ambiguity Markup Language (AML) reducing medical translation risks by 40%, and 3) an ontological framework reconciling Heideggerian linguistic essence with algorithmic probability through Derrida’s différance-encoded architectures. These interdisciplinary innovations bridge translation ethics, philosophy, and AI, offering actionable solutions for cultural DNA preservation and equitable cross-linguistic communication.

Taking the systematic logic of China's civil code as a framework, this paper explores the dynamic adaptation of the basic principles of civil law in the context of the digital economy era and its function of reconstructing the market economic order. The study points out that traditional civil law principles (such as autonomy of meaning, protection of reliance, and public order and good morals) are facing the adaptive challenges of new business models such as the platform economy and data transactions, and there is an urgent need to realise a balance of values by integrating decentralised principles and constructing synergistic mechanisms. The article starts from the limitations of formal rationality in modern civil law, analyses the inevitability of modern civil law's shift towards substantiation, and emphasises the role of the expansion of the principle of good faith and the development of the typology of public order and good morals in regulating the risk of market failure. The Chinese Civil Code responds to the real needs of the digital economy, such as the protection of human dignity, the internalisation of environmental costs, and the distribution of platform responsibilities, through such innovations as the independent codification of personality rights, the introduction of the green principle, and the dynamic articulation of the special law on commercial affairs. In view of new challenges such as the dilemma of positioning data rights and interests and the regulation of algorithmic power, the study proposes to construct a two-tier rule of "consent-anonymisation" based on the principle of good faith, expand the scope of application of the principle of reliance in order to balance technological neutrality and transaction security, and realise a systematic distribution of platform responsibilities through the presumption of fault and differentiated attribution of responsibility. Distribution. This paper provides a theoretical framework and a practical path for the adjustment of the basic principles of civil law in the era of digital economy, which is of dual value for the improvement of the civil law theory system and the promotion of the rule of law in the market economy.

Can a continent largely excluded from the design and governance of artificial intelligence (AI) hope to shape AI’s deployment in its own media ecosystems? Two companion volumes, published simultane...