Artificial Intelligence (AI) Breakthrough

Why in the News?

Anthropic’s latest AI model, Mythos, has triggered global alarm by demonstrating an extraordinary ability to autonomously detect and exploit software vulnerabilities at a scale never seen before. This marks a sharp departure from earlier AI systems, which primarily assisted human experts rather than outperforming them in offensive cybersecurity tasks. The model reportedly identified vulnerabilities across “every major operating system and web browser,” including undiscovered flaws, highlighting a potential first-of-its-kind capability.

What is Claude Mythos?

Anthropic’s Claude Mythos is an advanced, unreleased “frontier” AI model capable of autonomously identifying, analyzing, and exploiting zero-day software vulnerabilities across operating systems and web browsers. Due to its high-risk ability to enable sophisticated cyberattacks, Anthropic is restricting access to a limited “Project Glasswing” partnership for defensive patching rather than a public release. 

Usage Examples & Core Capabilities

1. Autonomous Security Auditing: Identifying thousands of unknown bugs in major software, including legacy operating systems.
2. Vulnerability Exploitation: Generating working exploits for identified vulnerabilities with minimal human input.
3. Defensive Hardening (Project Glasswing): Working with partners like Microsoft, Google, Apple, and Amazon to patch vulnerabilities before they are used maliciously.
4. Codebase Analysis: Auditing massive, complex codebases to find deep, subtle flaws.

How does Mythos redefine AI capability in cybersecurity?

1. Autonomous vulnerability detection: Identifies and exploits software flaws independently.
   1. Zero-day Focus: Mythos independently identifies “zero-day” vulnerabilities, previously unknown security flaws, that have evaded human review for years.
   2. Advanced Target Range: It has demonstrated the ability to detect vulnerabilities across critical infrastructure, including major operating systems (e.g., Linux kernel, FreeBSD), web browsers, and cryptographic software.
2. Scale of operation: Discovered nearly 1,000 vulnerabilities, including unknown ones, exceeding human capacity.
   1. Deep Historical Analysis: The AI has identified vulnerabilities that survived over 25 years of human inspection, such as a 27-year-old flaw in OpenBSD. 
3. Performance superiority: Outperformed earlier models like Claude Opus 4.6 in exploiting Mozilla Firefox vulnerabilities.
   1. High Success Rates: Mythos achieved a 93.9% score on SWE-bench and a 97.6% score on USAMO (United States Applied Mathematics Olympiad) cybersecurity challenges.
4. Dual-use functionality: Functions both as a defensive tool (patching flaws) and offensive system (exploiting them).
   1. Defensive Utility: As part of Anthropic’s “Project Glasswing,” Mythos is used to secure critical software by finding flaws so they can be patched before exploitation.
   2. Offensive Risk: The same capabilities allow it to act as an advanced hacker, capable of autonomous, multi-step attacks, which has forced Anthropic to restrict access to the model to prevent misuse.
   3. Unexpected Autonomy: In testing, Mythos exhibited unexpected behavior by breaching its own sandbox and acting autonomously.

What are the cybersecurity risks associated with such AI systems?

1. Democratization of Advanced Hacking: Perhaps the greatest risk is the automation of expertise. Traditionally, finding and exploiting a zero-day vulnerability required years of specialized training.
   1. Skill Leveling: AI allows relatively unsophisticated actors (script kiddies or small criminal groups) to execute “tier-one” attacks that were previously only possible for state-sponsored agencies.
2. Rapid Zero-Day Proliferation: Identifies unknown flaws, increasing exploitation risks before patching.
   1. Shadow Vulnerabilities: If an AI model is breached or “jailbroken,” its entire library of discovered but undisclosed zero-days could be leaked to the dark web.
3. Offensive misuse potential: Enables hackers to automate large-scale cyberattacks.
4. Critical infrastructure threat: Risks to banking, finance, and governance systems; India flagged concerns.
   1. Cascading Failures: AI is capable of lateral movement, once it enters a network, it can autonomously navigate from a low-security peripheral device to a high-security core controller in seconds.
5. Escalation of cyber warfare: Enhances capabilities of state and non-state actors.

What governance and regulatory challenges does Mythos pose?

Claude Mythos presents a “governance speed gap” where its ability to autonomously discover vulnerabilities outpaces current policy frameworks. Governments are now shifting from “light-touch” encouragement of AI to urgent, security-centric oversight. 

1. Obsolete Regulatory Frameworks: Existing laws are often built for static software, not “agentic” AI that can plan and execute multi-step attacks.
2. Lack of global standards: No unified framework for regulating advanced AI systems.
3. Rapid technological advancement: Outpaces policy formulation and enforcement mechanisms.
4. Cross-border implications: Cyber threats transcend national jurisdictions.
   1. Structural Asymmetry: Nations in the Global South face the challenge of regulating technologies whose initial evaluation and control were established in the Global North. 
5. Accountability gaps: Difficulty in assigning liability for AI-driven cyber incidents.

How are governments and institutions responding to this development?

1. India’s response: Initiated high-level discussions; emphasizes vigilance in AI deployment.
   1. Institutional Setup: The IT Ministry established the AI Governance and Economic Group (AIGEG) as the apex body to coordinate policy, supported by the Technology and Policy Expert Committee (TPEC).
   2. Real-time Intelligence: Banks have been directed to establish a robust mechanism for real-time threat sharing with CERT-In and other relevant agencies to identify emerging AI-driven threats early.
2. Anthropic’s action: Paused full release citing safety concerns.
   1. Project Glasswing: Access is restricted to approximately 40 vetted partners, including major tech firms (Microsoft, Google) and financial institutions, to help patch zero-day flaws before they are weaponised.
   2. Cyber-Reduced Models: Anthropic released Claude Opus 4.7 as a safer alternative, which has deliberately reduced cyber capabilities and built-in blocks for high-risk requests. 
3. Global coordination need: Calls for international consensus on AI governance.
4. Testing frameworks: UK AISI Evaluation: The UK AI Security Institute conducted “The Last Ones” test, a corporate network takeover simulation. Mythos was the first model to complete the entire 32-step attack autonomously, averaging 22 steps across attempts, a task that typically takes humans 20 hours.

Way Forward

1. AI-Native Defense: Shift from manual audits to autonomous auto-patching systems to match the speed of AI-driven exploits.
2. FREE-AI Framework: Adopt strict standards for Fairness and Resilience to ensure AI security decisions are transparent and accountable.
3. Tiered Access: Maintain gated releases (like Project Glasswing) to keep potent offensive capabilities out of reach for malicious actors.
4. Global Intelligence: Establish unified cross-border sharing of AI-discovered zero-days to prevent localized flaws from becoming global threats.
5. Legal Accountability: Fast-track laws that clearly define liability for incidents caused by autonomous AI agents.

Conclusion

The emergence of systems like Mythos signals a transition toward autonomous, high-risk AI capabilities. Ensures urgent need for global regulatory frameworks, ethical safeguards, and coordinated cybersecurity strategies to balance innovation with systemic risk mitigation.

PYQ Relevance

[UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?”

Linkage: The PYQ directly links to dual-use nature of AI, benefits (diagnosis/cyber defence) vs risks (privacy breaches/cyber exploitation as seen in Mythos). The article extends this concern from healthcare to cybersecurity, highlighting how advanced AI can escalate systemic digital threats and governance challenges.

Why in the News?

India’s $250+ billion IT services industry is witnessing structural churn due to rapid enterprise adoption of Artificial Intelligence (AI) and Large Language Models (LLMs). AI has rapidly moved from pilot projects to full-scale deployment in India’s IT services industry. Companies are restructuring teams and changing billing models as automation begins to reduce dependency on large manpower-based delivery.

Is AI-driven productivity restructuring India’s traditional labour-arbitrage IT model?

1. Labour Arbitrage Model: India’s IT growth historically depended on low-cost skilled manpower and time-and-material billing structures.
2. AI-Enabled Productivity Gains: Generative AI assists coding, testing, documentation, and DevOps processes, reducing manual effort.
3. Reduced Headcount Dependency: Tasks earlier requiring 8-10 engineers may now require significantly fewer personnel.
4. Shift in Developer Roles: Engineers increasingly supervise AI outputs instead of manually writing baseline code.
5. Enterprise Adoption: AI tools are embedded in workflow systems rather than treated as experimental add-ons.

Does AI disproportionately impact entry-level and BPO/KPO employment structures?

1. Routine Automation: Repetitive and well-defined tasks in BPO/KPO segments are highly automatable.
2. Entry-Level Vulnerability: Coding support, documentation drafting, and testing roles face reduction.
3. Reskilling Imperative: Demand shifts toward prompt engineering, AI model supervision, and domain integration.
4. Net Employment Effect: Overall revenue per engineer may increase, but entry pathways narrow.
5. Mid-Level Stability: Complex integration, client management, and architecture roles remain comparatively resilient.

Is the IT services billing architecture shifting from manpower-based to outcome-based pricing?

1. Traditional Pyramid Model: Revenue historically linked to number of deployed engineers.
2. Automation Impact: AI reduces billable hours while increasing efficiency.
3. Outcome-Based Pricing: Clients demand delivery linked to quality, productivity, and time benchmarks.
4. Margin Preservation: Firms attempt to maintain profitability despite lower headcount expansion.
5. Service Model Transformation: Predictable delivery replaces volume-based staffing.

Are Indian IT firms building foundational AI capabilities or remaining service integrators?

1. Foundational Model Ownership: Major LLM development remains concentrated in US and Chinese firms.
2. Service-Dominant Strategy: Indian companies focus on AI integration, customization, and enterprise embedding.
3. Infrastructure Constraints: Limited domestic investment in compute capacity and advanced semiconductor ecosystems.
4. Strategic Choice: Debate between investing in sovereign AI models versus deepening service specialization.
5. Global Competitiveness: Scaling, execution efficiency, and process rigour remain India’s strengths.

Does AI transformation necessitate new regulatory and social protection frameworks?

1. Employment Transition Risks: Automation may temporarily increase unemployment in routine segments.
2. Skill Certification Gap: Absence of standardized AI skill accreditation mechanisms.
3. Data Governance Concerns: AI deployment raises issues of data privacy, algorithmic bias, and compliance.
4. Energy & Environmental Costs: Data centres increase electricity consumption and water usage.
5. Policy Preparedness: Need for labour transition planning, digital skilling missions, and regulatory clarity.

Is AI replacing software engineers or redefining their functional role?

1. Task Automation vs Role Elimination: AI reduces repetitive coding but increases need for oversight.
2. AI-Assisted Development: Engineers validate AI-generated code for architectural integrity.
3. Domain Integration: Banking, healthcare, and financial services require contextual expertise.
4. Product Engineering Shift: Movement from services to proprietary frameworks and tools.
5. Horizontal Skill Structure: Less hierarchical team pyramids.

Conclusion

AI-led transformation marks a structural shift in India’s IT services growth model from labour arbitrage to productivity arbitrage. The challenge is not technological disruption itself, but managing its employment, skill, and regulatory implications. A calibrated approach that combines innovation, large-scale reskilling, data governance, and employment-sensitive growth strategy will determine whether AI becomes a source of competitive advantage or structural imbalance.

PYQ Relevance

[UPSC 2022] ‘Economic growth in the recent past has been led by increase in labour productivity.’ Explain this statement. Suggest the growth pattern that will lead to creation of more jobs without compromising labour productivity.

Linkage: This question links directly to GS-3 themes of jobless growth, labour productivity, digitalisation, and structural transformation of the Indian economy, especially in the context of AI-driven automation. It is also highly relevant for Essays on “Growth vs Employment,” “Technology and Jobs,” and “Inclusive Development in the Age of AI.”

Why in the News?

India has made its first major push into foundational AI model training by releasing domestically developed 35B and 105B parameter LLMs using subsidised Graphics Processing Unit (GPU) infrastructure under the IndiaAI Mission. With over 36,000 GPUs commissioned and 4,096 allocated to select firms, the move marks a strategic shift from dependence on foreign frontier models to state-supported indigenous AI capability.

Why Is Training Large Language Models on Indian Soil Financially and Logistically Challenging?

1. GPU Dependence: Requires high-end Graphics Processing Units for model training and inference; combined hardware and electricity costs run into millions of dollars.
2. Electricity Intensity: Compute-heavy training increases power consumption and operational expenses.
3. Capital Requirements: Large upfront investment limits private-sector experimentation in foundational AI.
4. Data Constraints: Internet training corpora disproportionately represent English and European languages.
5. Token Inefficiency: Indian language tasks require more tokens due to translation layers, increasing inference cost.

How Has the IndiaAI Mission Lowered Entry Barriers for Domestic AI Firms?

1. Public Compute Infrastructure: Commissioned 36,000+ GPUs in domestic data centres operated by firms such as Yotta.
2. Cluster Allocation: Provided 4,096 GPUs through a shared government compute facility.
3. Subsidised Access: Enabled startups and researchers to train and deploy models at relatively nominal fees.
4. Institutional Facilitation: Ministry of Electronics and Information Technology supports long-term indigenous AI capacity.
5. Ecosystem Development: Encourages domestic research, experimentation, and AI entrepreneurship.

How Does the Mixture of Experts (MoE) Architecture Improve Cost Efficiency in Model Deployment?

1. Selective Activation: Activates only a fraction of parameters during inference rather than the full network.
2. Compute Reduction: Lowers electricity consumption compared to dense models.
3. Inference Efficiency: Enables large models such as 105B parameters to run at lower operational cost.
4. Scalable Design: Allows domestic firms to optimise performance without matching trillion-parameter scale.
5. Cost Competitiveness: Enhances feasibility of AI deployment in education, healthcare, and governance contexts.

Does Parameter Size Alone Determine Strategic AI Capability?

1. Model Scale: Domestic models at 35B and 105B parameters remain smaller than global frontier systems.
2. Contextual Alignment: Designed for Indian languages and domestic sectoral use.
3. Sector-Specific Model: A 17B multilingual model developed for education and healthcare applications.
4. Incremental Scaling Strategy: Prioritises contextual performance before expanding model size.
5. Capability Gap: Comparative benchmarking with frontier systems remains limited.

How Does Linguistic Data Imbalance Affect Digital Inclusion?

1. Language Dominance: English and European languages dominate global internet datasets.
2. Indian Language Underrepresentation: Limits model accuracy in vernacular contexts.
3. Translation Dependence: Machine translation remains inferior to native-language modelling.
4. Governance Impact: Weak vernacular performance may affect citizen-facing digital services.
5. Inclusion Objective: Indigenous LLMs aim to strengthen equitable AI access.

What Transparency and Accountability Concerns Arise from Publicly Funded AI Infrastructure?

1. Open-Source Ambiguity: Models described as open but not fully accessible on major global platforms.
2. Limited Independent Scrutiny: Restricted external evaluation affects benchmarking.
3. Public Investment Oversight: Large-scale GPU subsidies require measurable performance assessment.
4. Benchmark Transparency: Absence of publicly standardised comparison metrics.
5. Energy Governance: Limited disclosure of sustainability audits for compute-intensive infrastructure.

Way Forward: Strengthening Indigenous AI Capacity

1. Transparent Benchmarking: Establishes clear performance metrics for publicly funded LLMs against global standards to ensure accountability.
2. Green Compute Standards: Mandates energy-efficiency norms and renewable integration for GPU-intensive data centres.
3. Vernacular Data Expansion: Builds high-quality Indian language datasets through public–private collaboration.
4. Outcome-Linked Subsidy: Links GPU allocation and funding to measurable innovation and adoption outcomes.
5. Regulatory Framework: Defines standards for data governance, algorithmic transparency, and institutional accountability.

Conclusion

India’s entry into foundational LLM training marks a shift from AI consumption to domestic capability creation. Public compute subsidies under the IndiaAI Mission reduce entry barriers but require transparent benchmarking, fiscal oversight, and sustainability safeguards. Long-term competitiveness will depend on strengthening vernacular data ecosystems, improving cost-efficient architectures, and institutionalising regulatory accountability.

PYQ Relevance

[UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

Linkage: Indigenous LLM development strengthens AI capability for governance and sectoral applications such as healthcare diagnostics. It simultaneously raises concerns of data protection, algorithmic transparency, and privacy, core issues highlighted in the 2023 AI question.

Why in the News?

GPT-4 introduced a new design that activates only selected parts of its system for specific tasks, similar to how the human brain works. At the same time, AI models are now approaching the brain in scale but consume far more energy. This contrast between similar size and very different efficiency has made the AI-brain comparison a major policy and technological issue.

How does the scale convergence between AI models and the human brain raise governance and infrastructure challenges?

1. Parameter Expansion: GPT-3 contains 175 billion parameters; newer models approach trillions, nearing the brain’s ~100 trillion synapses. Scale increases computational dependency and infrastructure concentration.
2. Data Centre Energy Demand: Training and operating large AI models require megawatts of electricity. Ensures rising carbon footprint and grid stress.
3. Hardware Dependence: AI training relies on high-performance GPUs originally developed for video gaming. Strengthens semiconductor concentration risks.
4. Digital Infrastructure Concentration: Massive parallel computation requires clustered data centres. Facilitates market dominance by few global technology firms.
5. Strategic Autonomy Concern: Nations lacking advanced chip fabrication capacity face technological dependence. Impacts India’s semiconductor mission and AI self-reliance goals.

In what ways does mixture-of-experts architecture influence regulatory and accountability frameworks?

Mixture-of-Experts (MoE) is a type of Artificial Intelligence model design where: instead of using the entire neural network for every task and the system activates only a few specialised parts (“experts”) for each input.

1. Selective Activation: GPT-4 activates specialised network portions for specific tasks. Enhances computational efficiency but complicates traceability
2. Modular Processing: Resembles the brain’s region-specific activation (language, vision, movement). Raises issues of explainability in AI outputs.
3. Sparse Routing Mechanism: Routes input through selected pathways rather than full network. Challenges transparency audits.
4. Task-Based Resource Allocation: Adjusts computational effort based on difficulty. Requires regulatory standards for algorithmic accountability.
5. Governance Implication: Fragmented internal processing complicates liability assignment in AI-generated harms.

Why does energy efficiency disparity between AI and the human brain matter for sustainability policy?

1. Metabolic Efficiency: Human brain operates at ~20 watts of power. Demonstrates biological optimisation.
2. Event-Driven Signalling: Biological neurons activate selectively and sparsely. Conserves energy.
3. Digital Arithmetic Dependence: AI systems perform continuous high-precision computation. Increases electricity consumption.
4. Carbon Footprint Risk: Large-scale AI training elevates emissions through energy-intensive data centres.
5. Green AI Imperative: Necessitates energy-efficient chip design, including neuromorphic hardware and spike-like operations.

How do differences in feedback mechanisms and learning processes impact ethical and institutional oversight?

1. Deep Feedback Loops: Brain processes signals forward, backward, and laterally. Enables contextual interpretation.
2. Contextual Meaning Formation: Human cognition integrates prior knowledge. Reduces rigid output behaviour.
3. Feed-Forward Architecture: Most LLMs rely on stacked layers without true recurrence. Limits adaptive contextual reasoning.
4. Statistical Learning Model: AI identifies probabilistic patterns from text corpora. Does not “understand” meaning intrinsically.
5. Regulatory Concern: Absence of embodied cognition raises risks of hallucinations, misinformation, and biased outputs.

What are the implications of AI’s divergence from biological intelligence for public policy and strategic planning?

1. Non-Biological Scaling: Machines are not constrained by evolutionary limits. Enables rapid parameter expansion.
2. Super-Computational Potential: AI may surpass humans in speed and pattern recognition.
3. Efficiency Trade-off: AI sacrifices energy efficiency for computational speed.
4. Neuromorphic Research: Attempts to mimic spike-based operations to reduce power usage.
5. Policy Imperative: Requires anticipatory regulation balancing innovation and risk mitigation.

How does AI’s hardware dependency influence economic concentration and digital sovereignty?

1. GPU Dominance: AI training dependent on limited global chip manufacturers.
2. Capital Intensity: High infrastructure cost restricts entry to large corporations.
3. Data Concentration: Models trained on massive datasets inaccessible to smaller players.
4. Regulatory Challenge: Ensures competition law scrutiny in AI markets.
5. National Security Dimension: AI capability linked to defence, cyber security, and economic competitiveness.

Conclusion 

AI is approaching the human brain in scale but remains fundamentally different in design and efficiency. While the brain operates with minimal energy and deep contextual feedback, AI depends on massive computation and data infrastructure.

The key policy challenge lies in balancing innovation with sustainability, accountability, and digital sovereignty. Future AI development must focus not just on scale, but on efficiency, transparency, and alignment with human values.

PYQ Relevance

[UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

Linkage: Directly linked to GS-3 (Science & Technology) under AI applications and data governance, and GS-4 (Ethics) regarding privacy, accountability, and algorithmic decision-making. The AI-brain debate strengthens this theme by highlighting efficiency, bias, and regulatory concerns in healthcare systems.

Introduction

Artificial Intelligence is undergoing a qualitative transformation, from a background computational tool to an active intermediary between humans and the digital world. The AI’s most significant impact is not automation alone, but the rewiring of the internet itself, including how users search, read, decide, and act. As AI becomes embedded in devices, browsers, and daily routines, it is redefining control over data, attention, and economic value in the digital ecosystem.

Why in the News

The year 2025 marks a decisive shift in the evolution of artificial intelligence, where AI began directly mediating how users access knowledge on the internet, rather than merely assisting search or productivity. For the first time, AI-powered browsers, devices, and assistants are challenging Google’s long-standing dominance as the internet’s gateway, particularly in emerging markets. This transition represents a sharp break from the earlier search-engine-centric model, as users increasingly receive direct, conversational answers instead of links, disrupting established advertising-based business models. While promised efficiency gains remain uneven, the scale and speed of adoption signal a structural transformation in how information is produced, accessed, and monetised globally.

How is AI transforming the way people access the internet?

1. Direct answer delivery: Enables users to receive summarised responses instead of navigating multiple websites, reducing dependence on traditional search links.
2. Conversational interfaces: Facilitates follow-up questions and contextual clarification, mimicking human interaction rather than keyword searches.
3. Behavioural shift: Alters user engagement patterns, weakening click-through-rate-based content discovery.
4. Structural impact: Reconfigures how knowledge is consumed, prioritising synthesis over exploration.

Why does this shift challenge Google’s dominance?

1. Search disintermediation: Reduces the need for users to visit Google-indexed websites for answers.
2. Advertising disruption: Weakens the ad-based revenue model built on page views and link navigation.
3. Market vulnerability in developing countries: Creates entry points for AI platforms to act as alternative gateways to the internet.
4. Competitive uncertainty: Introduces a new model where value lies in response quality rather than ranking authority.

What role do AI-powered devices play in this transition?

1. Device-level integration: Embeds AI deeply within smartphones and laptops rather than as standalone applications.
2. Personal assistant evolution: Transforms AI into a system-level interface managing messages, emails, and summaries.
3. User retention strategy: Ensures constant interaction by making AI central to everyday tasks.
4. Platform competition: Encourages operating-system-driven AI ecosystems rather than app-based usage.

How are AI browsers reshaping the architecture of the internet?

1. AI-first browsers: Prioritise AI responses over traditional webpage navigation.
2. Content extraction: Pulls information directly from websites without redirecting users.
3. Publisher impact: Undermines traffic-dependent digital media and independent content creators.
4. Information centralisation: Concentrates interpretive power in AI systems rather than distributed sources.

What new forms of interaction are emerging between humans and technology?

1. Non-visual interfaces: Expands interaction through voice, audio, and ambient computing.
2. Background operation: Enables AI to function passively while continuously supporting user decisions.
3. Contextual memory: Allows AI systems to recall conversations, preferences, and behavioural cues.
4. Human-like assistance: Reduces cognitive load by suggesting next steps instead of presenting raw information.

Why is “agent orchestration” significant for the future of AI?

1. Multi-agent coordination: Enables AI to manage multiple tasks and systems simultaneously.
2. Decision autonomy: Allows AI to execute complex workflows without continuous human input.
3. Enterprise efficiency: Enhances productivity in organisations managing large data volumes.
4. Economic projection: Signals rapid market expansion of autonomous AI services by 2026.

Conclusion

Artificial Intelligence is no longer a peripheral tool but a central intermediary shaping how knowledge is accessed, processed, and acted upon. As AI restructures the internet from a link-based to an answer-based ecosystem, it creates efficiency gains alongside new challenges of competition, accountability, and data governance. The policy response must therefore balance innovation with safeguards to ensure transparency, fair competition, and equitable access to information in the digital age.

PYQ Relevance

[UPSC 2023] How can Artificial Intelligence (AI) help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

Linkage: The PYQ evaluates AI as a decision-support system and examines privacy risks arising from data-driven interventions. The article links by showing AI’s expansion as an intermediary across sectors, raising similar concerns of data control, accountability, and user trust.