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AI Law Model for Ethical Regulation: A Multidisciplinary Framework and Strategic Recommendations for Governing Artificial Intelligence Even the most ambitious AI acts in leading jurisdictions (USA, EU, PRC and others) leave critical blind spots — from vague and contradictory definitions and fragmented ethics to the lack of deep assessment of socio-psychological and cultural impacts. AI Law Model for Ethical Regulation is a deliberate, research-driven paradigm designed to surface systemic flaws in existing and draft regulations and to offer a practical model for preventing foreseeable risks. The aim is not to copy and paste foreign templates, but to shape a regulatory architecture adapted to national contexts and grounded in interdisciplinary expertise and comprehensive risk analysis. We treat large language models and other classes of AI as a multidimensional socio-technical phenomenon with long-term legal, ethical, social, cultural, medical and economic consequences. Our core stance: AI legislation cannot be authored by a narrow circle of initiators. Effective norm-making must bring together legal scholars and practitioners, system architects and developers, cybersecurity and risk professionals, sociologists, psychologists, physicians, ethicists, and digital identity & resilience experts. Only integrated teams can capture the full spectrum of direct and indirect risks and anticipate long-horizon societal and political effects. The study combines principles (human-centricity, transparency, accountability, non-discrimination, cyber-resilience) with analytical blocks that map recurrent problem areas across jurisdictions, illustrate negative externalities when principles are ignored, and formulate actionable recommendations for legislators and regulators. Highlighted critical gaps: Enforcement & accountability. Absent or weak mechanisms allow actors to minimize or evade liability for direct and indirect harms (economic, ethical, social). Unified risk scale. No scientifically grounded, normatively anchored risk taxonomy, enabling strategic misclassification to dilute obligations. Public oversight & independent audit. A need for transparent, legally mandated mechanisms for continuous monitoring, objective assessment and public reporting on AI compliance. Vulnerable groups & cultural impacts. Regulations must address psychological, emotional and sociocultural effects on children, older adults, persons with disabilities and other sensitive cohorts to avoid deepening inequality. Standards & protocols. Insufficient incorporation of internationally recognized technical standards risks legal/technical fragmentation and poor interoperability. #AI #AILaw #EthicalAI #AIRegulation #AlgorithmicGovernance #DigitalRights #CyberResilience #RiskManagement #DigitalIdentity #GovTech https://lnkd.in/eTXTbrBA