15 March 2024 : The Hindu Editorial Notes PDF

The Hindu Editorial

15-March-2024

1. In issuing AI advisory, MEITY becomes a deity

Topic: GS2 – Governance – Government policies – Issues arising out of their design & implementation This topic is crucial for UPSC aspirants as it addresses the legal ambiguity and policy challenges in India’s AI technology regulation.

Context

The article critiques MEITY’s regulatory overreach and ambiguous advisories, particularly its recent attempt to govern AI, highlighting the decline in administrative standards and the shift towards digital authoritarianism in India.

 Background on MEITY and Its Regulatory Attempts:

• Formerly known as DEITY, the Ministry of Electronics and Information Technology (MEITY) faced ridicule due to its overzealous attempts to regulate the internet.
• MEITY has issued multiple advisories since March 2020, demanding vague censorship without clear legal authority, creating ambiguity around compliance.

Ambiguous Legal Basis of Advisories:

• MEITY’s advisories lack a clear legal basis under the Information Technology Act, 2000 (IT Act), leading to confusion regarding their enforceability.
• Despite being titled as “advisories,” they imply compliance without clear penalties, creating a compliance charade rather than encouraging dialogue.

Escalation in Regulatory Actions:

• Recent advisories on generative AI mark an escalation, triggered by viral incidents and media cycles, lacking sober assessment.
• MEITY’s transparency is opportunistic, withholding full advisory texts and relying on press releases and social media posts for communication.

Introduction of Illegal AI Governance Model:

• The March 1, 2024 advisory introduces an illegal AI governance model, requiring licensing of AI models, without clear definitions or legal basis.
• Official information is selectively distributed, with Minister Chandrasekhar’s press interviews and social media feeds serving as primary sources.

Undefined Terms and Ministerial Responses:

• Terms such as “bias prevention” and “Indian internet” lack definitions, contributing to uncertainty and confusion.
• Minister Chandrasekhar’s responses on social media further compound uncertainty, with undefined terms and abrupt exemptions for startups.

Decline in Administrative Standards:

• MEITY’s reliance on advisories without amending IT Rules and modifying them through social media posts reflects a decline in administrative standards.
• Technology policy is influenced by short-term ministerial visibility and social media metrics rather than deliberative processes and stakeholder consultations.

Shift in Policy Environment:

• The current environment favours administrative arrogance over deliberation, with policy decisions influenced by press coverage and social media reactions.
• Expert and technical commentary is self-censored, signalling a shift towards digital authoritarianism and reduced tolerance for criticism.

Conclusion:

• Despite criticism and ridicule, MEITY’s regulatory attempts persist, creating a sense of helplessness among stakeholders.
• The current regulatory landscape resembles a deity demanding obedience rather than a competent governing body, highlighting the need for reform and accountability.

Artificial Intelligence regulation

Need for Regulation: Ethical concerns: AI systems raise ethical dilemmas regarding privacy, bias, and accountability, necessitating clear guidelines. Safety and security: Regulation is necessary to ensure AI systems are safe, secure, and not susceptible to malicious use. Economic impact: Regulation can foster innovation by providing a framework for responsible AI development and deployment. Public trust: Regulation can build public trust by addressing concerns about AI’s potential negative impacts on society. Challenges: Rapid advancement: AI technology evolves quickly, making it challenging for regulations to keep pace with developments. Complexity: AI systems can be complex and opaque, making it difficult to assess their behaviour and potential risks. International cooperation: AI regulation requires international cooperation due to the global nature of AI development and deployment. Balancing innovation and regulation: Striking a balance between fostering innovation and protecting against potential harms is a challenge. Way Forward: Risk-based approach: Implement regulations that are proportional to the risks posed by AI systems. Multi-stakeholder engagement: Involve various stakeholders, including industry, academia, government, and civil society, in the development of AI regulations. Ethical frameworks: Develop ethical guidelines to ensure AI systems are developed and used in a manner consistent with societal values. Continuous monitoring and adaptation: Regularly review and update regulations to keep pace with advancements in AI technology and address emerging challenges. International standards: Work towards establishing international standards and norms for AI regulation to facilitate cooperation and consistency across borders.

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:  Discuss the implications of MEITY’s ambiguous regulatory advisories on technology governance and its impact on India’s policy environment. (150 Words /10 marks)

2. Has poverty really dropped to 5% in India?

Topic: GS2 – Social Justice – Issues relating to poverty and hunger The topic is crucial for UPSC as it addresses poverty measurement complexities, data reliability, and socioeconomic disparities in India.

Context

The article discusses debates over poverty measurement in India, including critiques of the poverty line, data reliability, consumption patterns, and nutritional considerations.

 Defining Poverty Line and Its Relevance:

• Poverty line in India is determined based on consumption expenditure, with the Tendulkar poverty line being commonly referenced.
• According to one viewpoint, the poverty level has reduced significantly over the years, suggesting a need to reconsider the poverty line.

Critiques on Poverty Line and Data Quality:

• Critics highlight the inadequacy of the current poverty line, emphasizing its conceptual flaws.
• There is no officially declared income poverty line by the government, leading to ambiguity.
• Questions are raised regarding the reliability of government data, citing instances of data suppression and politicization.

Discrepancy between Consumption and Income Growth:

• Claims of increased consumption expenditure are defended, stating that it has risen significantly in real terms over the past decade.
• However, counter arguments point out minimal real wage growth and increased unpaid labour participation, particularly among women.

Distributional Issues and Consumption Patterns:

• Concerns are raised about skewed consumption growth favouring certain segments of the population, with stagnant demand for mass consumption goods.
• GDP growth driven by capital expenditure hasn’t translated into increased private investment due to stagnant mass consumption demand.

Data Reliability and Politicization:

• Concerns are raised about the quality of data, particularly questioning the accuracy of certain labour force participation rates.
• Assertions are made regarding the politicisation of data in India, citing instances of data suppression and manipulation.

Nutritional Considerations and Poverty Assessment:

• The importance of nutrition in poverty assessment is underscored, citing a UN report stating that a majority of the population cannot afford a minimum nutritious diet.
• Arguments suggest a shift towards more sophisticated indicators beyond simple consumption measures.

Conclusion:

• Ongoing debates surrounding poverty measurement in India highlight the complexity of the issue.
• Challenges include the adequacy of the poverty line, data reliability, and distributional concerns.
• Addressing these challenges requires transparent and unbiased data collection methods, as well as a nuanced understanding of consumption patterns and nutritional considerations in poverty assessment.

Lack of Reliability of Government Data

Impact: Misinformed policies: Decisions based on unreliable data may lead to ineffective or counterproductive policies. Public mistrust: Inaccurate information erodes public trust in government institutions and undermines their legitimacy. Economic repercussions: Businesses rely on accurate data for planning and investment decisions; unreliable data can lead to economic instability. Social inequality: Flawed data can exacerbate existing social inequalities by misrepresenting the needs of marginalised communities. Way Forward: Transparency: Governments should be transparent about their data collection methods and sources to increase accountability. Quality assurance: Implement rigorous quality assurance measures to ensure data accuracy and reliability. Independent audits: Conduct regular audits by independent agencies to verify the accuracy of government data. Capacity building: Invest in training and resources for data collection, analysis, and management within government agencies. Collaboration: Foster partnerships with academic institutions, NGOs, and private sector entities to improve data collection and analysis. Public engagement: Involve citizens in the data collection process through crowdsourcing or citizen science initiatives to enhance data accuracy and relevance. Technology adoption: Embrace advanced technologies like AI and machine learning for data validation and analysis to improve reliability. Legal framework: Establish clear legal frameworks for data collection, storage, and dissemination to protect privacy rights and ensure data integrity.

PYQ: ‘Despite implementation of various programmes for eradication of poverty by the government in India, poverty is still existing.’ Explain by giving reasons. (150 words/10m) (UPSC CSE (M) GS-1 2018)

Practice Question:  Discuss the challenges in accurately measuring poverty in India, considering data reliability, consumption patterns, and socioeconomic disparities. (250 Words /15 marks)

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