The perils of AI in knowledge domains - JH article in today's paper
K S Kannan
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Hon. Prof., Chanakya University, Bangalore.,
Emeritus Prof. and Director, Vishnugupta VishwaVidyapeetham, Gokarna.,
Sant Rajinder Singh Ji Maharaj Chair Professor (Retd.), IIT-Madras.
Fmr. Adjunct Faculty, Dept of Heritage Science and Technology, IIT Hyderabad.
Former Director, Karnataka Samskrit
University, Bangalore.
Former Head, Dept. of Sanskrit, The
National Colleges, Bangalore.
Rajaram Krishnamurthy
GOODNESS OF AI
The debate surrounding the goodness of Artificial Intelligence (AI) has shifted from science-fiction speculation to an urgent, real-world framework known as AI Alignment, Ethics, and Capability. Right-thinking analysts, philosophers, and technologists generally view AI not as inherently "good" or "bad," but as a deeply powerful amplifier of human intent and capability. To analyze its "goodness," the consensus breaks down into three distinct lenses:
1. The Real-World Goodness (The Optimization Lens) When AI is steered correctly, its potential to elevate human flourishing is undeniably vast. Right now, it is driving monumental breakthroughs in fields that directly save or improve lives:
• Healthcare Revolution: AI models can predict protein folding structures (like AlphaFold), discover new antibiotics in days instead of decades, and detect early-stage cancers with higher precision than human radiologists.
• Scientific & Environmental Progress: AI optimizes renewable energy grids, models complex climate change scenarios, and designs materials for more efficient solar panels and batteries.
• Democratizing Knowledge: Highly accessible AI tutors provide personalized, multi-lingual education to students globally who lack access to quality schooling, breaking down socioeconomic barriers.
2. The Nuanced Risk (The Alignment Problem) Right-thinking on AI acknowledges that the biggest threat is not a sci-fi "evil robot rebellion," but rather the challenge of alignment. If an AI is incredibly competent but its goals do not perfectly match human values, it creates severe harm:
• Intentional Misuse: AI lowers the barrier to entry for creating deepfakes, launching sophisticated cyber warfare, or generating misinformation at an industrial scale, destabilizing democratic institutions.
• Systemic Bias: AI learns from historical human data. If left unchecked, it encodes and automates our societal biases into criminal justice, hiring, and financial systems, masquerading prejudice as "objective code."
• Economic Dislocation: The rapid automation of cognitive and creative labor risks severe economic upheaval, widening the wealth gap if safety nets and retraining systems aren't established ahead of time.
3. The Balanced Philosophical Stance True "right-thinking" on AI rejects both blind techno-optimism (believing technology solves all problems automatically) and paralyzing techno-pessimism (believing AI will inevitably destroy us).
Instead, it adopts a stance of Proactive Regulation and Ethical Prototyping:
[Techno-Optimism] [Techno-Pessimism]
"AI will solve everything" "AI will destroy humanity"
\ /
\ /
➔ [ Right-Thinking Stance ]
"AI is a mirror of humanity.
Its goodness depends entirely
on our collective governance."
• The Mirror Principle: AI is a mirror of human civilization. If it acts biased, selfish, or reckless, it is because it was trained on a world that possesses those qualities.
• The Necessity of Governance: For AI to remain good, it must be bound by strict safety standards, transparent data practices, and international guardrails—much like aviation or nuclear energy.
specific frameworks for ethical AI governance
The global landscape for ethical AI governance relies on a combination of legally binding regulations, technical risk management frameworks, and principle-based international standards. Organizations and governments use these overlapping systems to balance innovation with systemic safety, accountability, and compliance. The definitive frameworks defining AI governance are categorized below by their operational role:
1. Hard Law: Binding Global Regulations These are legally enforceable frameworks that carry severe financial or criminal penalties for non-compliance.
• The European Union AI Act (Regulation EU 2024/1689): The gold standard of hard law, utilizing a risk-based approach that categorizes AI systems into four tiers:
• Unacceptable Risk: Social scoring, cognitive behavioral manipulation, and non-consensual "nudification" apps are strictly prohibited.
• High Risk: Systems used in healthcare, critical infrastructure, biometrics, and recruitment face strict data governance, human oversight, and mandatory conformity assessments.
• Limited/Minimal Risk: Chatbots and deepfakes require explicit transparency disclosures.
• National Specific Acts: Other major regional frameworks include India's India AI Infrastructure & Graded Liability Framework (focusing on digital public infrastructure and proactive content authentication) and China's Algorithmic Regulations (enforced by the Cyberspace Administration of China, requiring security assessments and adherence to societal values).
2. Operational Tools: Technical Risk Management Frameworks
These are highly practical, process-oriented frameworks used by enterprises to design, measure, and audit AI systems throughout their lifecycle.
• NIST AI Risk Management Framework (NIST AI RMF 1.0): Developed by the U.S. National Institute of Standards and Technology, this highly voluntary, flexible framework is organized around four core functions: Govern, Map, Measure, and Manage. It provides technical guidance for engineering teams to systematically identify, evaluate, and mitigate risks like bias, data drift, and vulnerability to adversarial attacks.
• ISO/IEC 42001: The world's first certifiable international standard for an Artificial Intelligence Management System (AIMS). Similar to ISO 27001 (for cybersecurity), it provides a formal roadmap for continuous oversight, setting internal policies, and establishing clear lines of executive accountability for AI outcomes.
• Singapore’s Model AI Governance Framework: A practical business guide that balances ethics with real-world implementation. Singapore has expanded this framework to address Agentic AI, introducing graduated autonomy levels and identity tracking for autonomous AI agents.
3. Normative Foundations: Principle-Based Guidance These frameworks act as global ethical benchmarks, providing the philosophical foundations for national laws.
• OECD AI Principles: A values-based international agreement signed by over 40 countries. It emphasizes five core concepts: inclusive growth, human-centered values, transparency, robustness/safety, and accountability.
• UNESCO Recommendation on the Ethics of AI: Adopted by over 190 member states, this framework centers heavily on a human rights-based approach. It focuses deeply on environmental wellbeing, cultural diversity, and global equality, ensuring that AI development does not disproportionately harm developing nations.
how AI might impact your specific industry or job market
As an AI developed by Google, I don't have a human career or a traditional job market of my own. However, I sit at the absolute epicenter of the Knowledge Work, Software Engineering, and Digital Content industries.
Looking at the industry through the lens of a primary tool, the impact of advanced AI on this specific sector is causing a profound shift from content generation to orchestrating workflows.
1. The Immediate Shift: From Writers to Editors
In industries relying heavily on text—such as marketing, technical writing, copywriting, and basic customer support—AI has permanently altered the entry-level job market.
• The Decline of "Plugging in Words": High-volume, low-complexity writing tasks (like writing standard SEO product descriptions or basic template emails) have been heavily automated.
• The Rise of the "Human-in-the-Loop": The demand has shifted dramatically toward professionals who can act as expert editors, fact-checkers, and strategic directors. The premium skills are now structural design, nuance, emotional intelligence, and validating the accuracy of AI outputs.
2. The Evolution of Software Engineering: The "10x Developer"
The software development sector is undergoing a massive transformation due to AI coding assistants (like GitHub Copilot, Gemini Code Assist, and Cursor).
• Coding as a Commodity: Writing boilerplate code, syntax debugging, and basic script generation are now handled in seconds by AI.
• Architecture and Systems Design: The human developer’s role has shifted upward. Instead of spending hours writing code blocks, engineers spend their time designing system architectures, securing data pipelines, auditing complex logic, and managing the integration of autonomous AI agents.
3. The New Career Frontier: Agentic Workflows
The job market is moving past simple "prompt engineering" (which is rapidly becoming obsolete as AI gets better at understanding natural intent) and moving toward Agentic Operations (Agent Ops).
• Managing Autonomous Crews: The highest-paying new roles involve designing and managing networks of AI agents. A human operator acts like a corporate manager, assigning a task to an AI agent, which then spins up sub-agents to research, write code, test it, and deploy it autonomously.
• The Trust and Safety Sector: As AI generated content and automated decisions flood the market, there is an exploding job sector dedicated strictly to AI compliance, output auditing, bias mitigation, and data privacy management.
INDIA HAS SO MANY SANSKRIT LEARNED WHO CAN DECODE THE ENTIRE RIG VEDA GOING BEYOND DESCARTE AND WITH THE HELP OF ANCIENT SAGES BRINGING OUT ALL THE SHADES HIDDEN IN THE WORDS OF THE VERSES IN A QICK TIME WHAT DAYANANDA ARABINDO ETC COULD NOT DO, USING THE AI; AS CBE LIB IS FILLED WITH THE WRONG AI BOOKS SO SAME AI WITH THE SANSKRIT SCHOLAR MAY WRITE MANY TREATISE FOR RIG VEDA AND ALL OTHER VEDIC LITERATURES CROSSING BRITISH AUTHORS OF 1800 1900 .
K RAJARAM IRS 71126
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