LIJDLR

EU AI Act

REGULATORY FRAMEWORK AND POLICY DEVELOPMENTS FOR ARTIFICIAL INTELLIGENCE

Volume IV Special Issue I /

REGULATORY FRAMEWORK AND POLICY DEVELOPMENTS FOR ARTIFICIAL INTELLIGENCE Veronica Gabriel Fernandes, LLM, Second Year ,KES Shri. Jayantilal H. Patel Law College (India) Download Manuscript doi.org/10.70183/lijdlr.2026.v4sil.38 Artificial Intelligence (AI) has become an integral part of modern society, influencing decision-making in sectors such as healthcare, banking, law enforcement, education, and governance. The integration of AI into these domains has led to improved efficiency, enhanced accuracy, and greater innovation. While AI offers efficiency and innovation, its unchecked use may result in serious legal and ethical challenges, including privacy violations, discrimination, lack of transparency, and absence of accountability. This research paper examines the regulatory frameworks and recent policy developments governing Artificial Intelligence at international and national levels. It analyses significant legal instruments such as the European Union’s Artificial Intelligence Act, policy-based approaches adopted by the United States, regulatory measures in China, and the evolving Indian legal position. The paper also discusses key concerns relating to data protection, transparency, ethical AI, and liability. The paper highlights the need for balanced and adaptive regulation that safeguards fundamental rights while promoting innovation.  The paper further explores key challenges associated with AI regulation, such as data protection and privacy safeguards, transparency and explainability of algorithms, ethical deployment of AI systems, and the determination of liability for harm caused by AI-driven decisions. By employing doctrinal legal research and drawing upon constitutional principles and judicial precedents, the study emphasises the necessity of a balanced, flexible, and human-centric regulatory framework. It ultimately advocates a harmonised, risk-based approach to AI governance that effectively safeguards fundamental rights while fostering responsible innovation and technological progress. The study concludes by recommending a risk-based, human-centric, and harmonized approach to AI governance.

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AI, CREDIBILITY, AND EVIDENCE IN ASYLUM LAW: DIALECT RECOGNITION, TRANSCRIPT SUMMARISATION, DOCUMENT ANALYSIS, AND COUNTRY-OF-ORIGIN RESEARCH

Volume IV Issue II /

AI, CREDIBILITY, AND EVIDENCE IN ASYLUM LAW: DIALECT RECOGNITION, TRANSCRIPT SUMMARISATION, DOCUMENT ANALYSIS, AND COUNTRY-OF-ORIGIN RESEARCH Rajeev Meena, LL.M. (Business Law), University of California, Davis School of Law, California, USA. Advocate enrolled with the Bar Council of Rajasthan, India. Legal Researcher and AI Legal Evaluation Specialist focusing on the intersection of Law, Artificial Intelligence, Intellectual Property, and Legal Education Download Manuscript doi.org/10.70183/lijdlr.2026.v04.225 Artificial intelligence is increasingly entering refugee status determination through tools such as dialect recognition, name transliteration, speech transcription, transcript summarisation, document analysis, country-of-origin research, and case matching. These tools are often presented as instruments of efficiency, consistency, and administrative support. Yet, in asylum law, they operate within a field where proof is already fragile and credibility is often decisive. Applicants may flee without documents, lose evidence during displacement, face trauma-related memory gaps, or remain unable to obtain corroboration from unsafe States. In such conditions, AI-shaped evidence may not merely assist decision-makers. It may silently influence how truth, identity, origin, and risk are understood. This paper examines the legal reliability standard that should govern AI-assisted evidence in asylum adjudication. It argues that technical accuracy alone cannot justify evidentiary reliance. Asylum decisions require legal trustworthiness, which must include explainability, traceability, data quality, contestability, human oversight, and protection against sole or decisive reliance on automated outputs. The paper analyses the doctrinal foundation of credibility assessment, the benefit of doubt principle, evidentiary vulnerability of asylum seekers, and the risks of administrative over-reliance on technical tools. It further evaluates the EU AI Act, especially its classification of asylum-related AI systems as high-risk, and considers its relationship with asylum law safeguards, non-refoulement, individual assessment, and the right to an effective remedy. The paper concludes that AI may assist asylum decision-making, but it must never replace human legal judgment. In refugee protection, technology must remain subordinate to fairness, reasons, and the duty to protect people from persecution and serious harm.

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THE EU AI ACT AND THE RIGHT TO ASYLUM: ARE “HIGH-RISK” SAFEGUARDS ENOUGH FOR ASYLUM, VISA, AND RESIDENCE DECISIONS?

Volume IV Issue II /

THE EU AI ACT AND THE RIGHT TO ASYLUM: ARE “HIGH-RISK” SAFEGUARDS ENOUGH FOR ASYLUM, VISA, AND RESIDENCE DECISIONS? Prabin Acharya, Advocate licensed to practice in Nepal Legal Researcher and Immigration Law Professional; LL.M., University of California, Davis School of Law (2025) Download Manuscript doi.org/10.70183/lijdlr.2026.v04.224 This paper examines whether the EU AI Act’s “high-risk” framework adequately protects the right to asylum when artificial intelligence assists asylum, visa, and residence decisions. It situates AI within EU migration governance, where automated screening, document analysis, risk indicators, country-of-origin research, and credibility tools may influence access to protection before a human officer gives reasons. The paper argues that the AI Act marks an important regulatory advance because it expressly classifies several migration, asylum, and border-control AI systems as high-risk and subjects them to duties of risk management, data governance, transparency, human oversight, accuracy, and fundamental rights assessment. Yet these safeguards remain incomplete if they operate only as technical compliance standards. Asylum law demands individualised assessment, meaningful reasons, effective remedy, and strict respect for non-refoulement under the EU Charter, the Refugee Convention, and the Common European Asylum System. The paper further contends that opacity, automation bias, weak disclosure, and predictive profiling may convert AI assistance into disguised determinative decision-making. It therefore proposes a rights-centred model requiring notice, explainability, independent audit, case-file traceability, and stronger limits on AI tools that affect credibility, evidence reliability, or removal outcomes. Its central claim is that technology must remain subordinate to protection and due process.

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