Establish strong, enforceable legal frameworks to ensure

The gap

Evidence profile

Sourced from the recommendations and future work of the source papers, classified as general, spanning 2 journals.

Supporting evidence — 3 representative gaps

• Artificial Intelligence–Driven Digital Transformation: A Critical Analysis of Governance, Policy Frameworks, and Emerging Perspectives (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  7. Establish strong, enforceable legal frameworks to ensure accountability, transparency, and ethical use of AI in governance systems. 8. Adopt risk-based and adaptive regulatory approaches to balance innovation with safety and evolving technological challenges. 9. Promote explainable and transparent AI systems with clear accountability mechanisms to strengthen democratic oversight. 10. Invest in capacity building, technical expertise, and digital infrastructure to support effective AI adoption in governance. 11. Strengthen data protection laws and privacy safeguards to prevent misuse and protect citizens’ rights effectively.

  generalrecommendationsevidence 5/5
  Keywords: accountability governance systems strengthen establish strong enforceable legal frameworks ensure transparency ethical adopt risk based
• Role of Artificial Intelligence in Child Development: A Legal Perspective (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  7.1 Enact a Child-Centric AI Regulation India should enact a dedicated legal framework for AI, with a specific chapter on child protection. This framework should: • Prohibit AI systems that use subliminal techniques to manipulate children or that exploit child vulnerabilities. • Classify AI systems used in education, healthcare, and child welfare as "high risk," requiring conformity assessments and ongoing monitoring.53 • Mandate child rights impact assessments for any AI system likely to interact with children. 7.2 Strengthen the DPDP Act for AI Contexts The DPDP Act, 2023 should be amended to: 49F. Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (Harvard University Press, 2015) 6–8. 50M. Gupta, "Age Assurance in India: Legal and Technical Challenges," (2023) 11 Indian Journal of Data Protection 45, 52. 51DPIIT, Report of the Committee on Non-Personal Data Governance Framework (2020) para 5.12. 52Global Partnership on AI, Member Countries, https://gpai.ai/members/ (last visited 20 May 2026). 53AI Act, supra note 42, Article 6, Annex III. 200 | P a g e Cognitive Thinking: An International Journal of Interdisciplinary Studies Vol. 2, Issue-2 (April-June, 2026), pp.192-204, ISSN: 3107-5088, www.cognitivethinking.in • Define "automated decision-making" and "algorithmic profiling" and provide children with a right not to be subject to solely automated decisions with significant effects.⁷¹ • Require Data Protection Impact Assessments specifically addressing AI risks to children. • Provide for a right to meaningful explanation when an AI system makes a decision affecting a child. 7.3 Establish a Statutory Liability Regime India should enact a statute creating strict liability for AI developers and deployers for developmental harm caused to children. The statute should: • Reverse the burden of proof for causation where the AI system is opaque.54 • Allow for class action lawsuits on behalf of affected children. • Establish a no-fault compensation fund for AI-related child harm, funded by contributions from AI developers. 7.4 Create Specialized Adjudicatory Mechanisms The government should establish: • A specialized tribunal within the National Human Rights Commission or the National Commission for Protection of Child Rights (NCPCR) to hear AI-related child harm cases. • A technical expert panel to assist courts with evidentiary issues concerning AI systems. 7.5 Mandate Age Assurance and Child-Friendly Design The Ministry of Electronics and Information Technology (MeitY) should issue binding rules requiring: • Proportionate age assurance mechanisms for services likely to be accessed by children, • balancing privacy with protection. "Child-friendly design" standards, disclosures in age-appropriate language, and easy-to-use reporting mechanisms. including default high-privacy settings, transparent 7.6 Integrate AI Literacy into Education The NEP 2020 implementation should include: • AI literacy as part of the school curriculum, teaching children how algorithms work, how data is collected, and how to exercise their rights. • Training for teachers and parents on identifying and mitigating AI-related risks to children. 7.7 Pursue International Cooperation India should actively participate in: • Negotiations for a binding international treaty on AI and child rights, potentially under the auspices of UNESCO or the UN Human Rights Council. • Bilateral and multilateral agreements for cross-border enforcement of judgments involving AI- caused child harm.

  generalrecommendationsevidence 5/5
  Keywords: child children rights protection india harm enact framework systems assessments system assurance international establish related
• Racing Ahead, Governing Behind: An Institutional Analysis of AI Governance Readiness in Global Capability Centres (2026) · International Journal of Applied Information Systems · doi

  For GCC IS governance leaders, the five structural antecedents constitute a prospective readiness diagnostic applicable before governance incidents occur. For parent organisation boards, the study demonstrates that uniform enterprise governance standards transferred without adaptation for host-country regulatory environments will reliably produce the readiness gaps documented. For regulators, the multi-jurisdictional complexity documented in P5 argues for coordinated guidance between DPDPA and international regulatory counterparts. The SEC's approach to cybersecurity governance disclosure [23] provides a useful model for regulators seeking to mandate board-level AI governance engagement. enforcement, CERT-In [19], 17 International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, Delaware, USA Volume 13– No. 3, June 2026 – www.ijais.org include Three principal limitations bound this study. First, as a multi- site field study, analytical generalisation is to theoretical propositions rather than statistical populations [38]; the five propositions require quantitative testing across larger GCC samples. Second, the cross-sectional design captures configurations at a single point in time. Third, India's DPDPA 2023 enforcement guidance continues to evolve. Future research directions longitudinal study of GCC governance configurations, quantitative survey research testing P1-P5, comparative study across GCC geographies, and dedicated empirical study of agentic AI governance design requirements. 6. CONCLUSION AI governance configurations in GCCs and the relationships between governance levels across different organisational and institutional contexts have received scant attention in academic research. The present study addresses this gap through an in- depth field study of 28 interviews across five GCC organisations, applying the constrained-efficiency framework [1], NIST AI RMF 1.0 [7], and Gioia et al. [6] qualitative methodology.

  generalfuture workevidence 5/5
  Keywords: governance across five configurations readiness regulatory documented regulators multi guidance dpdpa international enforcement ijais field

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