1. MEITY turns into a god by releasing an AI advisory.
Topic: GS2 – Governance – Government policies – Issues arising out of their design & implementation
As it tackles the legal ambiguity and policy challenges in India’s AI technology regulation, this topic is essential reading for UPSC candidates.
| Context |
| – The article highlights the erosion of administrative standards and the move towards digital authoritarianism in India, criticising MEITY’s regulatory overreach and vague advice, especially in relation to its recent attempt to regulate AI. |
Background on MEITY and Its Regulatory Attempts:
- The Ministry of Electronics and Information Technology (MEITY), formerly known as DEITY, was made fun of for its zealous attempts to control the internet.
- Since March 2020, MEITY has issued several advisories requiring nebulous censorship without providing clear legal authority, which has left compliance unclear.
Ambiguous Legal Basis of Advisories:
- The Information Technology Act, 2000 (IT Act) does not provide a clear legal foundation for MEITY’s advisories, which creates uncertainty about their enforceability.
- Even though they are called “advisories,” they suggest compliance without providing explicit consequences, which encourages compliance rather than fostering conversation.’
Escalation in Regulatory Actions:
- Recent warnings about generative AI indicate a rise brought on by media cycles and viral events that lack a serious analysis.
- Withholding complete advisory texts and depending instead on press releases and social media posts for communication, MEITY’s transparency is opportunistic.
Introduction of Illegal AI Governance Model:
- With no clear legal foundation or definitions, the advisory from March 1, 2024 introduces an illegal AI governance model that requires licencing of AI models.
- Press interviews and social media posts by Minister Chandrasekhar are the main sources of information, although official sources are selectively released.
Undefined Terms and Ministerial Responses:
- There is ambiguity and confusion surrounding terms like “Indian internet” and “bias prevention,” which lack definitions.
- Minister Chandrasekhar’s comments on social media, which include vague language and sudden exemptions for startups, increase uncertainty.
Decline in Administrative Standards:
- MEITY’s dependence on advisories in lieu of revising IT Rules and posting updates on social media is indicative of a deterioration in administrative standards.
- Short-term ministerial visibility and social media metrics, as opposed to deliberative processes and stakeholder consultations, shape technology policy.
Shift in Policy Environment:
- Press coverage and social media responses impact policy decisions, favouring administrative arrogance over thoughtful consideration.
- Self-censored expert and technical commentary indicates a move towards digital authoritarianism and a decrease in tolerance for dissent.
Conclusion:
- MEITY’s regulatory attempts continue in spite of mockery and criticism, leaving stakeholders feeling powerless.
- The current regulatory environment emphasises the need for accountability and reform because it resembles a deity demanding obedience rather than a capable governing body.
| Artificial Intelligence regulation |
| Need for Regulation: – Ethical issues: AI systems present questions of privacy, bias, and accountability that call for explicit regulations. – Safety and security: In order to guarantee that AI systems are secure, safe, and impervious to nefarious use, regulation is required. – Economic impact: By offering a framework for responsible AI development and deployment, regulation can promote innovation. – Public trust: By resolving worries about AI’s possible detrimental effects on society, regulation can increase public trust. Challenges: – Fast advancement: Because AI technology is developing so quickly, it is difficult for regulations to keep up with the latest advancements. – Complexity: It can be challenging to evaluate the behaviour and possible risks of AI systems due to their complexity and opaque nature. – International cooperation: Because AI is developed and used globally, international cooperation is necessary for AI regulation. – Regulation and innovation: It can be difficult to strike a balance between promoting innovation and guarding against possible risks. Way Forward: – Risk-based strategy: Put in place laws that are commensurate with the threats that artificial intelligence systems pose. – Multi-stakeholder engagement: Involve a range of stakeholders in the creation of AI regulations, such as business, academia, government, and civil society. – Frameworks for ethics: To guarantee that AI systems are created and applied in a way that is compatible with society values, ethical guidelines should be developed. – Constant observation and adjustment: To stay up to date with AI developments and tackle new issues, regulations should be reviewed and updated on a regular basis. – International standards: To promote collaboration and uniformity across national boundaries, work towards the establishment of international standards and norms for AI regulation. |
| PYQ: Explain what artificial intelligence (AI) is. How is clinical diagnosis aided by AI? Do you think that using Al in healthcare poses a threat to an individual’s privacy? (150 words, 10 seconds) (CSE (M) GS-3 2023) UPSC |
| Practice Question: Talk about how India’s policy environment is affected by MEITY’s unclear regulatory advisories and how they affect technology governance. (10 marks, 150 words) |
2. Has India’s poverty rate truly decreased to 5%?
Topic: GS2 – Social Justice – Issues relating to poverty and hunger
Given that it tackles issues of data reliability, socioeconomic disparities in India, and the complexities of measuring poverty, the topic is critical to UPSC.
| Context |
| – The article addresses discussions surrounding the measurement of poverty in India, encompassing criticisms of the poverty line, data dependability, consumption trends, and nutritional aspects. |
Defining Poverty Line and Its Relevance:
- In India, the Tendulkar poverty line is frequently used as a point of reference when determining the poverty line based on consumption expenditure.
- One point of view suggests that the poverty line needs to be reevaluated because the level of poverty has decreased significantly over time.
Critiques on Poverty Line and Data Quality:
- Critics draw attention to the current poverty line’s shortcomings and shortcomings in theory.
- There is uncertainty because the government has not established an official income poverty line.
- There are concerns expressed about the accuracy of government data, based on examples of politicisation and data suppression.
Discrepancy between Consumption and Income Growth:
- Arguments in favour of higher consumption expenditure assert that during the last ten years, real expenditures have increased dramatically.
- Counterarguments, on the other hand, highlight the small real wage growth and rising participation in unpaid labour, especially among women.
Distributional Issues and Consumption Patterns:
- There are worries about the demand for mass-consumption goods remaining stagnant and skewed consumption growth favouring specific population segments.
- Due to stagnating mass consumption demand, GDP growth spurred by capital expenditure has not resulted in increased private investment.
Data Reliability and Politicization:
- The accuracy of some labour force participation rates is being questioned, raising concerns about the quality of the data.
- Claims are made about the politicisation of data in India, referencing cases of data manipulation and suppression.
Nutritional Considerations and Poverty Assessment:
- Citing a UN report that states most people cannot afford a minimum nutritious diet, the importance of nutrition in assessing poverty is emphasised.
- Arguments point to a move away from basic consumption measurements and towards more complex indicators.
Conclusion:
- Debates about poverty measurement in India are still going strong, which emphasises how complicated the problem is.
- The poverty line’s suitability, data dependability, and distributional issues are among the difficulties.
- To tackle these obstacles, open and objective data gathering techniques are needed, along with a sophisticated comprehension of consumption trends and nutritional factors in poverty estimation.
| Lack of Reliability of Government Data |
| Impact: – Informed policies: Ineffective or counterproductive policies may result from decisions made based on untrustworthy data. – Public mistrust: False information damages the credibility of government agencies and erodes public confidence in them. – Consequences for the economy: Companies need accurate data to plan and make investments; inaccurate data can cause instability in the economy. – Social inequality: By misrepresenting the needs of marginalised communities, faulty data can worsen already-existing social inequalities. Way Forward: – Transparency: To improve accountability, governments should be open and honest about the sources and techniques of the data they collect. – Enforce stringent quality assurance procedures to guarantee the dependability and accuracy of the data. – Independent audits: To ensure the accuracy of government data, conduct routine audits by impartial organisations. – Building capacity within government agencies can be achieved through funding tools and training for data management, analysis, and collection. – Cooperation: To enhance data collection and analysis, cultivate alliances with educational institutions, non-profit organisations, and businesses. – Public engagement: To improve data accuracy and relevance, involve citizens in the data collection process through citizen science initiatives or crowdsourcing. – Adoption of advanced technologies: To increase reliability, embrace AI and machine learning for data analysis and validation. – Legal framework: To preserve individual privacy rights and guarantee data integrity, clearly define the laws governing the gathering, storing, and sharing of data. |
| PYQ: Poverty persists in India despite the government’s implementation of numerous programmes aimed at ending the issue. Provide justifications for your explanation. (150 words, 10 seconds) (UPSC GS-1 2018 CSE (M)) |
| Practice Question: Talk about the difficulties in precisely calculating poverty in India while taking socioeconomic disparities, consumption patterns, and data reliability into account. (Fifteen marks, 250 words) |
Leave a Reply