23 November 2024 : The Hindu Editorial Analysis

1. Understanding the changing face of extremist violence

(Source – The Hindu, International Edition – Page No. – 6)

Topic: GS3 – Internal Security

Context

extends to India’s experience with both left-wing and right-wing extremist movements.

Introduction: Conflicts Eclipsing Other Forms of Violence

• The ongoing Ukraine war and the Israel-Hamas conflict have overshadowed other extremist violence globally.
• New trends in extremist violence, their consequences, and the aspects of these trends are being overlooked, which may have future implications.

The RAND Study and Its Relevance

• A RAND study in the U.S. titled “Changing Face of Hate, Domestic Extremist Violence” highlighted changes in domestic terrorism and violent extremism.
• The study emphasised the need for law enforcement to understand evolving extremist trends.
• Though focused on the U.S., it carries lessons for other nations, including India.

Post-Independence Extremism in India

• Early years after Independence saw communal violence and revolutionary Communist movements like the Tebhaga Movement in Bengal and the Telangana uprising in the late 1940s.
• While these movements were suppressed, the revolutionary spirit persisted.

Rise of the Naxalite Movement

The late 1960s to the late 20th century saw the emergence of the Naxalite Movement, driven by left-wing extremism. It initially attracted educated youth but eventually devolved into violence. The movement spread across states like West Bengal, Andhra Pradesh, and Kerala. Vigilance is necessary as such ideologies can resurface And destabilise the nation.

Shift to Right-Wing Extremism Globally

• Left-wing ideologies are being replaced by right-wing philosophies, especially in Europe.
• Examples include:
  • Germany is becoming increasingly xenophobic.
  • France grappling with right-wing concessions.
  • Brexit in the U.K., signalling a shift towards right-wing ideologies.
• Right-wing ideologies are no longer fringe elements, posing risks to democracy and sovereignty.

Radicalization and Mobilization Post-2001

• The 9/11 attacks led to the largest right-wing (Jihadist) mobilisation in the West, altering the threat landscape.
• Social factors like misinformation, disinformation, and the COVID-19 pandemic further fueled right-wing extremism.
• The rise of the Islamic State added another dimension, influencing West Asia and other regions.

Cross-Pollination of Right-Wing Extremism

• There is growing interaction between extremist ideologies, leading to violent extremism globally.
• Law enforcement must remain prepared for these evolving threats.

Right-Wing Terrorism in India

• India has faced protests against government actions targeting groups like the Popular Front of India (PFI) and Social Democratic Party of India (SDPI), which are associated with right-wing Muslim extremism.
• The rise of right-wing terror modules among various sections of society, including youth and women, has raised concerns.
• Harsh measures may be necessary but must be handled with care to balance security and liberty.

Conclusion

• Both right-wing and left-wing extremist threats demand constant vigilance.
• Security agencies must adapt to the evolving threat landscape to safeguard democracy and societal stability.

PYQ: Naxalism is a social, economic and developmental issue manifesting as a violent internal security threat. In this context, discuss the emerging issues and suggest a multilayered strategy to tackle the menace of Naxalism. (UPSC CSE (M) GS-3 2022)

Practice Question:  Discuss the evolving nature of extremist ideologies globally, with a focus on the rise of right-wing extremism. Analyse its implications for national security and democracy, particularly in the Indian context. (150 Words /10 marks)

2. Democratising AI needs a radically different approach

(Source – The Hindu, International Edition – Page No. – 4)

Topic: GS3 – Science and Technology

Context

The dominance of Big Tech in the AI ecosystem stems from their vast computational resources, data monopolies, and end-to-end service offerings. Current approaches, like open data platforms and public infrastructure, fail to address these entrenched advantages. A paradigm shift toward smaller, theory-driven AI models is crucial to democratize AI development effectively.

Concerns Over Big Tech’s AI Dominance

• Big Tech companies hold significant power over the AI ecosystem due to their extensive computational resources, tools, and data access.
• Efforts by countries like India to democratize AI development through sovereign cloud infrastructure, open data platforms, and support for local start-ups may inadvertently reinforce Big Tech’s dominance.

Challenges of Big Tech Dominance

• High Computational Costs:
  • Deep learning, the dominant form of AI, requires significant computational resources.
  • Training advanced models is prohibitively expensive; for instance, Gemini Ultra cost $200 million in 2023.
  • New entrants often rely on Big Tech for compute credits, perpetuating dependency.
• Advocacy for Larger Models:
  • Big Tech promotes deep learning and larger models to solidify its dominance and generate revenue streams.
• Comprehensive Service Offerings:
  • Big Tech’s tools, optimized for their cloud infrastructure, simplify development workflows and reduce costs.
  • They provide end-to-end services, including data preparation, labelling, and access to the latest AI models, making it difficult for competitors to match.
• Data Monopoly:
  • Big Tech benefits from continuous and diverse data streams, creating unparalleled “data intelligence.”
  • Smaller AI companies often end up selling to Big Tech, further entrenching its monopoly.
• Declining Role of Academia:
  • With the shift toward deep learning, industry players have overtaken academia in AI research, publications, and citations, shaping the field’s direction.

Shortcomings of Current Policy Proposals

• Investments in public compute infrastructure and federated models, inspired by India’s Digital Public Infrastructure, are insufficient.
• These initiatives need to be competitive with Big Tech’s offerings and address its entrenched advantages in tools, algorithms, and data.
• Open data platforms often face “commercial capture,” where Big Tech leverages its superior resources to dominate.

Need for a Radical Approach: Prioritising a Theory of Change

• A paradigm shift is required to break away from the “bigger is better” mindset of Big Data and deep learning.
• AI development should prioritize:
  • Theory-driven Models: Rely on causal mechanisms and hypothesis testing rather than statistical patterns from large datasets.
  • Purpose-driven AI: Smaller, targeted models informed by domain expertise and lived experiences.
  • Progressive Change Frameworks: Data collection should be curated to test and refine theories of change, enabling more democratic AI development.
• Historical advancements in fields like medicine and aviation relied on hypothesis-driven approaches, demonstrating the importance of scientific rigour over sheer data volume.

A Missed Opportunity to Challenge Big Tech

• The Global Development Compact aimed to democratise AI but failed to reimagine the paradigm.
• It adhered to the flawed assumption that large datasets and computational power would address Big Tech monopolies and achieve Sustainable Development Goals.
• Without rejecting the deep learning-centric approach, dependence on Big Tech will only increase.

Conclusion

• To counter Big Tech’s dominance, AI development must embrace smaller, theory-driven models aligned with democratic and progressive goals.
• Urgent action is needed to change the current AI trajectory and prevent further monopolisation by Big Tech.

PYQ: 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 Al in healthcare? (150 words/10m) (UPSC CSE (M) GS-3 2023)

Practice Question:  Examine the challenges posed by Big Tech’s dominance in the AI ecosystem and critically evaluate the role of policy measures in addressing these issues. Suggest alternative approaches to ensure a more equitable AI development framework. (250 Words /15 marks)