The Hindu Newspaper Analysis

Editorial Topic : Putting the Brakes on “Bulldozer Justice”

 GS-2 Mains Exam : Polity

Revision Notes

Introduction

• Extra-Legal Demolitions: Concerns over lack of due process and disregard for judicial directives.
• Supreme Court Intervention: Court seeks suggestions for pan-India guidelines.

Key Concerns

• Discriminatory Justice: Selective demolitions targeting vulnerable groups.
• Marginalization: Inequality and conflict intensifying marginalization of certain communities.
• Limiting Executive Power: Need for guidelines to re-imagine the legal framework.

Legality of Punitive Demolitions

• Large-Scale Demolition Drives: Used as collective punishment for rioters.
• Bypassing Due Process: Quick actions without following legal procedures.
• Tit for Tat Approach: Demolicions as a political brand for State governments.
• Violating Fundamental Rights: Demolictions undermine criminal laws and violate fundamental rights.
• Supreme Court Intervention: Moratorium on punitive demolitions and strict tripartite procedure for legitimate demolitions.

 

Key Aspects for Guidelines

• UN Guidelines: The United Nations Basic Principles and Guidelines prescribe directives for addressing displacement.
• Need for Comprehensive Guidelines: Supreme Court’s task to formulate pan-India guidelines.

• Demolitions as a Last Resort: Demolitions should be carried out only in exceptional circumstances.
• Accurate Classification of Law: Define types of buildings and surrounding circumstances for demolitions.
• Balance Between Rights and Actions: Assess state action and right to adequate housing and resettlement.
• Understanding Patterns of Demolitions: Analyze data to identify patterns and gaps in the process.

Procedural Steps

• Pre-Demolition Phase:
  • Burden of proof on authorities to justify demolition.
  • Reasoned notice with information on land records and resettlement plans.
  • Independent committee review.
  • Stakeholder engagement and addressing vulnerable groups.
  • Sufficient time for affected individuals.
• During Demolition Phase:
  • Minimize use of physical force and heavy machinery.
  • Presence of government officials.
  • Pre-decided demolition time.
• Rehabilitation Phase:
  • Adequate and proper rehabilitation.
  • Grievance redress mechanism.
  • Remedies such as compensation, restitution, and return to original home.

Way Forward: Affixing Personal Liability

• Lack of Due Process: Consistent disregard for judicial directives.
• Impunity for Officials: “Good faith” clauses in municipal laws protect officials.
• Personal Liability: Explore ways to hold officials accountable for forced evictions and demolitions.

Conclusion

• Pan-India Guidelines: Essential for ensuring due process and accountability.
• Sensitization of Law Enforcement: Train officials to follow existing directives.
• Personal Liability: Affix personal liability on those who order forced evictions.
• Checks and Balances: Implement measures to ensure accountability and balance in demolition practices.

 

 

 

 

 

The Hindu Newspaper Analysis

Editorial Topic : AI in Health Care: A Bold but Cautious Approach for India

 GS-2 Mains Exam : Health

Revision Notes

Context:

• India must address foundational issues in its health system before embracing AI-driven healthcare.

Introduction:

• Ambitious idea of “free AI-powered primary-care physician for every Indian, 24/7” within five years.
• Key concerns include feasibility, sustainability, and India’s readiness to undertake such a massive project.

Importance of Primary Health Care (PHC):

• PHC is the backbone of health systems, ensuring access to essential health services.
• Integrated services: Brings health services closer to communities.
• Broader health determinants: Tackles wider determinants like sanitation, nutrition, and education through multisectoral action.
• Empowerment: Encourages individuals to manage their own health, promoting active participation.

Risks of Adopting AI in PHC:

• Impersonal care: AI may turn patients into passive recipients rather than active participants.
• Undermining the human element: AI lacks empathy, human intelligence, and cultural understanding.

AI vs. Human Intelligence in Health Care:

• Human skills: AI excels in repetitive tasks but struggles with understanding complex real-world contexts, memory, and reasoning.
• Patient care nuances: Medicine requires an understanding of patient-specific complexities that go beyond AI’s capabilities.
• Ethical reasoning: AI lacks the moral and ethical reasoning that comes from human consciousness.
• Data issues: Health-care data is often scattered, incomplete, and inaccessible, making AI training difficult.

Case Study: Naegele’s Rule in Obstetrics

• Traditional model: Used for over 200 years to predict childbirth dates.
• Accuracy issues: Has a mere 4% accuracy, failing to account for critical factors like maternal age, nutrition, race, etc.
• AI Paradox: Better predictive models require vast personal data, raising privacy and ethical concerns.

Challenges in AI Adoption in Health Care:

1. Data Requirements:

• Data complexities: Health data is personal and varies across populations, making it difficult to standardise.
• Diversity in India: India’s demographic diversity complicates the creation of a unified AI model, requiring extensive and contextual data.

2. Infrastructure Costs:

• High investment needed: Significant infrastructure is required to capture, collect, and train AI models.
• Recurrent costs: AI models need constant fine-tuning, especially as health factors evolve over time.

3. Ethical Concerns:

• Black box problem: AI decisions are often not transparent, posing risks when used in critical healthcare decisions.
• Trust issues: Without clear understanding, healthcare providers may hesitate to rely on AI, and wrong recommendations could lead to harmful outcomes.

AI’s Role in Specific Healthcare Tasks:

• Narrow intelligence applications: AI is effective in narrow tasks like hospital logistics (managing supplies, waste, drug procurement).
• Diffusion models: Useful for pattern recognition in tasks like screening histopathology slides or identifying subsets of populations for medical imaging.
• Large Language Models (LLMs) & Large Multimodal Models (LMMs):
  • Supporting medical education and research by providing rapid access to information and simulating patient interactions.
  • Can be used to simulate complex clinical scenarios for health-care professionals’ training.

The Real-Life Stakes of AI in Health Care:

• Real-life concerns vs. reel-life success: AI feats like Google DeepMind’s Go game victory are acceptable in games but raise concerns when applied to human health.
• Health risks: Mistakes in AI-driven health decisions could be life-threatening, making transparency and reliability paramount.

AI Governance and Ethical Concerns in India:

• Ethical exploitation: A Kenyan petition against OpenAI’s ChatGPT revealed exploitation of underpaid workers in AI training. This raises concerns about vulnerable populations being exploited for AI data collection in India.
• Patient data protection: Legal frameworks are needed to protect patient data from misuse. The data used to train AI models rightfully belongs to patients.

Global Context: The EU’s Artificial Intelligence Act:

• India lacks comprehensive AI regulations like the European Union Artificial Intelligence Act. Such frameworks are crucial to ensure ethical AI deployment in health care.

Costs and Sustainability of AI in Health Care:

• Continuous investment: Developing advanced AI models requires ongoing investment in research, data infrastructure, and updates.
• Who will bear the costs? Ensuring affordable AI-driven healthcare will require careful consideration of long-term financial sustainability.

Way Forward:

• Regulation and ethics: India must prioritise developing AI regulation and addressing ethical concerns in healthcare.
• Data standardisation challenges: While population-level data can be useful, it risks ecological fallacy if not properly contextualised.
• Prioritising patient safety: AI should follow the core medical ethics principle of “Do No Harm.”

Conclusion:

• AI potential: AI in healthcare promises enhanced efficiency and reduced error rates, but foundational issues in India’s health system must be addressed first.
• Inclusive AI development: Ensuring a more inclusive and ethical AI approach will help overcome current challenges and align India with global best practices.
• Investment in infrastructure: Significant investments in data infrastructure, research, and continuous updates are required, but caution is needed to avoid compromising patient safety and ethical standards.