Avinash Kumar Ágarwal

Avinash Agarwal, Peeyush Agarwal, Manisha J. Nene

As Artificial Intelligence becomes increasingly embedded in critical telecommunications infrastructure, existing legal frameworks remain ill-equipped to address the distinct risks this development introduces. This paper proposes the AI Regulatory Readiness Index (ARRI), a reproducible instrument for doctrinally assessing the legal preparedness of national frameworks to govern AI in critical digital infrastructure, and applies it across ten jurisdictions spanning five continents. ARRI comprises seven indicators across three dimensions: substantive AI-specific obligations, operational safeguards, and governance coordination, scored on a four-point ordinal scale and aggregated to a normalised 0-100 index. Legal instruments in force as of 28 February 2026 are assessed across telecommunications, cybersecurity, data protection, and AI governance domains. The study finds that global AI regulatory readiness in telecommunications remains concentrated in the lower range, with a mean ARRI score of 34 and a median of 26.5. AI incident reporting and risk classification emerge as the most acute and near-universal gaps, with binding legal definitions of AI-specific incidents largely absent across the legal frameworks applicable to telecommunications in the jurisdictions studied. ARRI scores diverge systematically from existing composite indices. For example, Indonesia achieves ITU Global Cybersecurity Index Tier 1 status yet scores 19 under ARRI, demonstrating that cybersecurity readiness and AI regulatory readiness are legally distinct conditions that existing frameworks conflate. The ten jurisdictions are classified into five regulatory archetypes, and a normative minimum standards framework is proposed, anchoring baseline AI governance readiness at an ARRI score of 67. ARRI is designed to be sector-portable and applicable beyond telecommunications to energy, healthcare, and transport infrastructure.

Avinash Agarwal, Harsh Agarwal

Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on multiple factors such as context, use case, type of the AI system, and so on. In this paper, we elaborate that the AI system is prone to biases at every stage of its lifecycle, from inception to its usage, and that all stages require due attention for mitigating AI bias. We need a standardised approach to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot research topic, a holistic strategy for AI fairness is generally missing. Most researchers focus only on a few facets of AI model-building. Peer review shows excessive focus on biases in the datasets, fairness metrics, and algorithmic bias. In the process, other aspects affecting AI fairness get ignored. The solution proposed: We propose a comprehensive approach in the form of a novel seven-layer model, inspired by the Open System Interconnection (OSI) model, to standardise AI fairness handling. Despite the differences in the various aspects, most AI systems have similar model-building stages. The proposed model splits the AI system lifecycle into seven abstraction layers, each corresponding to a well-defined AI model-building or usage stage. We also provide checklists for each layer and deliberate on potential sources of bias in each layer and their mitigation methodologies. This work will facilitate layer-wise standardisation of AI fairness rules and benchmarking parameters.

Avinash Agarwal, Manisha J. Nene

The rapid deployment of Artificial Intelligence (AI) in critical digital infrastructure introduces significant risks, necessitating a robust framework for systematically collecting AI incident data to prevent future incidents. Existing databases lack the granularity as well as the standardized structure required for consistent data collection and analysis, impeding effective incident management. This work proposes a standardized schema and taxonomy for AI incident databases, addressing these challenges by enabling detailed and structured documentation of AI incidents across sectors. Key contributions include developing a unified schema, introducing new fields such as incident severity, causes, and harms caused, and proposing a taxonomy for classifying AI incidents in critical digital infrastructure. The proposed solution facilitates more effective incident data collection and analysis, thus supporting evidence-based policymaking, enhancing industry safety measures, and promoting transparency. This work lays the foundation for a coordinated global response to AI incidents, ensuring trust, safety, and accountability in using AI across regions.

Avinash Agarwal, Manisha J. Nene

Purpose: The governance of artificial iintelligence (AI) systems requires a structured approach that connects high-level regulatory principles with practical implementation. Existing frameworks lack clarity on how regulations translate into conformity mechanisms, leading to gaps in compliance and enforcement. This paper addresses this critical gap in AI governance. Methodology/Approach: A five-layer AI governance framework is proposed, spanning from broad regulatory mandates to specific standards, assessment methodologies, and certification processes. By narrowing its scope through progressively focused layers, the framework provides a structured pathway to meet technical, regulatory, and ethical requirements. Its applicability is validated through two case studies on AI fairness and AI incident reporting. Findings: The case studies demonstrate the framework's ability to identify gaps in legal mandates, standardization, and implementation. It adapts to both global and region-specific AI governance needs, mapping regulatory mandates with practical applications to improve compliance and risk management. Practical Implications - By offering a clear and actionable roadmap, this work contributes to global AI governance by equipping policymakers, regulators, and industry stakeholders with a model to enhance compliance and risk management. Social Implications: The framework supports the development of policies that build public trust and promote the ethical use of AI for the benefit of society. Originality/Value: This study proposes a five-layer AI governance framework that bridges high-level regulatory mandates and implementation guidelines. Validated through case studies on AI fairness and incident reporting, it identifies gaps such as missing standardized assessment procedures and reporting mechanisms, providing a structured foundation for targeted governance measures.