Navigating AI’s Risks in India’s Digital Future

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Author: Denis Avetisyan

A new framework systematically maps the unique safety challenges posed by artificial intelligence within the growing landscape of Digital Public Goods in India.

This review presents an empirically-grounded taxonomy for AI safety risk assessment, tailored to the Indian context and focused on issues of bias and governance.

Existing global AI safety frameworks often overlook the nuanced socio-technical realities of rapidly developing economies. This paper introduces ‘Astra: AI Safety, Trust, & Risk Assessment’, a novel, empirically-grounded taxonomy designed to categorize AI safety risks specifically within the Indian context, addressing challenges like linguistic diversity and infrastructure gaps. The core of Astra is a tripartite causal taxonomy organizing 37 risk classes into Social and Frontier/Socio-Structural categories, focused on hazards mitigable through technical iteration. Can this bottom-up, inductive approach to AI risk assessment provide a scalable foundation for responsible innovation in emerging digital ecosystems?

Deconstructing Digital Dharma: AI Safety in the Indian Context

India’s ambitious adoption of AI-driven Digital Public Goods – spanning areas like healthcare, finance, and education – introduces safety challenges amplified by the sheer scale of implementation and the nation’s complex socio-economic landscape. Unlike deployments in smaller, more homogenous populations, the potential for algorithmic bias to exacerbate existing inequalities is significantly heightened when systems interact with hundreds of millions of citizens possessing diverse backgrounds, levels of digital literacy, and access to resources. This rapid scaling necessitates careful consideration of unintended consequences, particularly regarding data privacy, fairness, and accountability, as systemic errors or vulnerabilities could disproportionately impact marginalized communities and undermine the intended benefits of these public services. The unique context demands a shift from theoretical risk assessments to proactive, real-world evaluations that account for the specific vulnerabilities present within India’s digital ecosystem and societal fabric.

Current AI safety frameworks, largely developed in Western contexts, often fail to fully account for the socio-technical realities of India. Considerations such as linguistic diversity, digital literacy disparities, and the prevalence of informal data economies introduce unique vulnerabilities not adequately addressed by generalized safety protocols. For example, bias detection algorithms trained on predominantly Western datasets may perform poorly when applied to Indian languages or datasets reflecting different cultural norms. Furthermore, the rapid scaling of AI-powered Digital Public Goods – like those used for identity verification or financial inclusion – demands a localized approach to safety, one that prioritizes equitable access, data privacy within a complex regulatory landscape, and resilience against manipulation or misuse specific to the Indian context. Adaptation requires not simply translation of existing frameworks, but a fundamental rethinking of risk assessment and mitigation strategies to align with the nation’s unique challenges and opportunities.

The accelerating integration of artificial intelligence into India’s digital infrastructure demands a forward-looking assessment of potential societal impacts, particularly concerning existing inequalities. Simply importing established AI safety protocols risks overlooking vulnerabilities unique to the Indian context, such as disparities in digital literacy, access to technology, and socioeconomic status. A nuanced understanding of how algorithmic bias, data privacy issues, and automation could disproportionately affect marginalized communities is therefore essential before large-scale deployment. Failing to proactively identify and mitigate these harms could unintentionally amplify existing disadvantages, creating new forms of exclusion and hindering equitable access to the benefits of AI-driven Digital Public Goods. This necessitates a shift from reactive problem-solving to preventative measures, ensuring that technological advancements contribute to a more inclusive and just society.

Mapping the Labyrinth: An Indian AI Safety Risk Ontology

The Indian AI Safety Risk Ontology is a formalized system designed to represent potential harms arising from artificial intelligence, enabling a structured and systematic approach to risk assessment. This ontology moves beyond qualitative descriptions of risk by providing a defined taxonomy, allowing for consistent identification, categorization, and analysis of AI-related hazards. The formalized representation supports quantitative risk modeling and facilitates the development of standardized safety protocols, improving the reliability and comparability of risk assessments across different AI systems and applications. It serves as a foundational component for building comprehensive AI safety frameworks and informs mitigation strategies by explicitly detailing the nature of potential harms.

The Indian AI Safety Risk Ontology’s development employed both Grounded Theory and Causal Taxonomy methodologies to ensure a comprehensive and logically structured categorization of potential AI harms. Grounded Theory, an inductive approach, facilitated the identification of risks directly from data sources – primarily the AI Risk Repository – allowing categories to emerge from the evidence itself. Complementing this, Causal Taxonomy was utilized to establish hierarchical relationships between risks, mapping out causal pathways and dependencies. This combined approach enabled the ontology to move beyond simple risk identification to a nuanced understanding of how risks relate to one another, improving its utility for risk assessment and mitigation strategy development.

The Indian AI Safety Risk Ontology utilizes the AI Risk Repository as its primary data source for identifying potential harms associated with artificial intelligence systems. This reliance on a curated risk repository enables a structured approach to developing comprehensive AI Safety Frameworks. Consequently, the ontology classifies identified risks into 37 distinct, granular categories – termed “leaf-level classes” – allowing for detailed analysis and targeted mitigation strategies. These classes represent specific harm types and facilitate a standardized taxonomy for assessing and comparing risks across different AI applications and deployments.

Unveiling the Vectors: Diverse Threats to AI-Powered Public Goods

AI systems, particularly those employing complex machine learning models like deep neural networks, often exhibit limited interpretability, meaning the reasoning behind their outputs is opaque to human observers. This lack of transparency poses a significant safety risk, as it becomes difficult to identify the causes of errors or biases and to ensure consistent, reliable performance. Consequently, accountability for decisions made by these systems is compromised, especially in high-stakes applications where understanding the rationale is critical for legal, ethical, or safety reasons. The inability to trace the decision-making process hinders debugging, validation, and the establishment of trust in the AI system’s outputs.

AI systems can exhibit harmful behaviors stemming from biased training data or flawed algorithms, resulting in both bias and exclusion. Bias manifests as systematic and repeatable errors that create unfair outcomes for specific demographic groups, perpetuating existing societal inequalities in areas like loan applications, hiring processes, and criminal justice. Exclusion occurs when AI systems fail to adequately recognize or serve the needs of certain populations due to underrepresentation in training data or design limitations. Furthermore, these systems can generate toxic content, including hate speech, harassment, and misinformation, which can have detrimental effects on individuals and communities. Mitigation requires careful data curation, algorithmic fairness techniques, and ongoing monitoring for unintended consequences.

Large Language Models (LLMs) present specific risks related to factual accuracy and data security. LLMs are prone to “hallucination,” generating outputs that are syntactically correct but factually incorrect or nonsensical, which can undermine trust and reliability in applications such as information retrieval or automated decision-making. Furthermore, these models are vulnerable to security breaches, potentially exposing sensitive training data or allowing adversarial manipulation of outputs. Privacy risks also arise from the potential for LLMs to memorize and inadvertently disclose personally identifiable information (PII) present in their training datasets, necessitating robust data sanitization and privacy-preserving techniques. These issues are compounded by the scale of LLMs and the complexity of their internal workings, making detection and mitigation challenging.

Effective deployment of AI-powered public goods requires comprehensive situational awareness beyond initial resource allocation. Assuming consistent access to computational resources, data quality, and skilled personnel during operation can lead to systemic failures when confronted with real-world limitations such as network outages, data drift, or unexpected user behavior. Furthermore, the implementation of these systems carries socioeconomic risks, notably potential job displacement resulting from automation. Proactive mitigation strategies must address both the technical vulnerabilities related to resource dependency and the broader societal impact of workforce changes to ensure sustainable and equitable outcomes.

Echoes in the Machine: Implications for Governance and Future Directions

The increasing integration of artificial intelligence into Indian society necessitates the establishment of comprehensive governance mechanisms and ethical guidelines. Without these, the identified risks – ranging from algorithmic bias and data privacy violations to job displacement and the erosion of human agency – could disproportionately impact vulnerable populations and hinder equitable development. A proactive, multi-stakeholder approach is vital, involving policymakers, researchers, industry leaders, and civil society organizations to co-create frameworks that promote responsible AI innovation. These guidelines should not only address immediate harms but also anticipate future challenges, ensuring that AI systems align with India’s societal values and contribute to sustainable, inclusive growth. The absence of such oversight risks stifling public trust and hindering the potential benefits of AI for a nation poised to become a global leader in the technology.

A foundational step towards harnessing the benefits of artificial intelligence lies in anticipating potential harms through proactive risk assessment. The recently developed Indian AI Safety Risk Ontology provides a structured framework for identifying and categorizing these risks, moving beyond generalized concerns to pinpoint specific vulnerabilities within the Indian context. This ontology isn’t merely a descriptive tool; it enables a systematic approach to evaluating AI systems before deployment, allowing developers and policymakers to address potential issues related to bias, privacy, security, and societal impact. By leveraging this detailed understanding of possible failures, interventions can be designed to minimize negative consequences and foster public trust, ultimately paving the way for responsible innovation and the equitable distribution of AI-powered resources.

Sustained investigation into the identified risks, coupled with broad public education, represents a critical pathway toward establishing confidence in artificial intelligence systems. Addressing potential harms proactively-through research focused on mitigation strategies and transparent communication of those efforts-is not merely a technical challenge but a societal imperative. Building trust requires demonstrating a commitment to responsible AI development, ensuring that the benefits of these powerful technologies are widely accessible while simultaneously safeguarding against unintended consequences. Increased public understanding of both the capabilities and limitations of AI will be essential for fostering informed dialogue and shaping policies that promote ethical and equitable implementation, ultimately solidifying acceptance and maximizing the positive impact of AI on society.

The newly developed risk framework offers a scalable pathway for managing the complex challenges presented by artificial intelligence, enabling both responsible innovation and broader access to AI-driven public services. Through a meticulous process of identifying and categorizing potential harms, the framework delineates 37 distinct classes of risk – ranging from algorithmic bias and data privacy violations to societal disruption and economic inequality. This granular level of detail allows for targeted mitigation strategies and proactive policy development, moving beyond generalized concerns to address specific vulnerabilities. Consequently, the framework not only safeguards against potential negative consequences but also establishes a foundation for building public trust and ensuring that the benefits of AI are distributed equitably, ultimately fostering a more inclusive and sustainable AI ecosystem.

The pursuit of a robust AI risk assessment, as detailed in this paper concerning Digital Public Goods, mirrors a fundamental principle of systems analysis: to truly understand something, one must dissect it, test its boundaries, and expose its vulnerabilities. As Bertrand Russell observed, “To be happy, one must be able to recognize and appreciate beauty.” This echoes the need for a nuanced understanding of AI’s potential harms – recognizing the ‘beauty’ of a well-functioning system also necessitates acknowledging its inherent flaws and potential for misuse. The Astra framework, by meticulously categorizing AI safety risks, isn’t simply identifying problems; it’s engaging in a controlled demolition of assumptions, revealing the underlying design sins of these increasingly complex systems.

Where Do We Go From Here?

The systematization of risk-even within a constrained domain like Digital Public Goods-reveals a curious truth: categories are not containers. This taxonomy, while empirically grounded, inevitably creates the risks it attempts to define. A system built to anticipate harm also establishes the boundaries of what constitutes harm, potentially blinding itself to emergent, unpredictable failures. The very act of labelling bias, for instance, does not eliminate it; it merely shifts the problem into a measurable, and therefore, manageable, form – a comforting illusion, perhaps.

Future work must abandon the pursuit of comprehensive risk prediction. Instead, resources should focus on developing robust, adaptable systems capable of responding to unforeseen consequences. The Indian context, with its unique socio-technical landscape, presents a particularly valuable testing ground – a crucible where elegant, over-engineered solutions will inevitably falter, forcing a return to first principles.

Ultimately, the true metric of success won’t be the avoidance of failure, but the speed and grace with which a system recovers from it. A framework that anticipates every possible harm is a fantasy; one that learns from inevitable chaos, however, is a practical necessity. The goal, then, isn’t to build a perfect shield, but a supremely effective repair kit.

Original article: https://arxiv.org/pdf/2602.17357.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-20 11:06