16 August 2024 : The Hindu Editorial Analysis

1. An obstinate refusal to focus on welfare

(Source – The Hindu, International Edition – Page No. – 8)

Topic: GS2 – Governance – Government Policies

Context

The article critiques the Union Budget’s reduced focus on welfare schemes, highlighting declining allocations for crucial programs like MGNREGA and NFSA amidst widespread poverty and malnutrition. It contrasts the current government’s approach with the previous UPA era, emphasising the need for increased welfare spending to achieve true development.

Introduction

• The Union Budget has been severely criticised for failing to increase expenditure on critical welfare schemes that support marginalised populations.
• Despite 34% of the population surviving on less than ₹100 a day and over 81 crore people requiring free food grains, the government continues to reduce welfare allocations.

Key Welfare Schemes and Underfunding

• MGNREGA and the National Food Security Act (NFSA) are two of the government’s largest welfare schemes but have seen a continuous decline in Budget allocations as a share of GDP since 2014-15.
• MGNREGA guarantees 100 days of employment to every rural household, while Food Subsidy under NFSA provides free food grains to two-thirds of the population.
• In the recent Budget, NFSA’s expenditure dropped to 0.63% of GDP from 0.72% last year, despite a significant portion of the population being unable to afford a healthy diet.
• The MGNREGA allocation fell to 0.26% of GDP from 0.29% last year, exacerbating rural distress amidst stagnating real wages.
• Overall, these schemes now have a 25% lesser combined Budget allocation compared to 2014-15.

Neglect of Vulnerable Groups

• The National Social Assistance Programme (NSAP), which supports vulnerable groups such as widows, the elderly, and disabled individuals below the poverty line, saw no increase in its Budget allocation.
• The allocation for NSAP has halved as a share of GDP since 2014-15, from 0.06% to 0.03%.
• The scheme provides paltry pensions of ₹200 a month to the elderly and ₹300 to widows, amounts unchanged since 2006.

Welfare and Nutrition Schemes

• Saksham Anganwadi and Poshan 2.0, aimed at tackling child malnutrition, have seen a significant reduction in Budget allocation.
• The allocation for these programs has declined by more than half since 2014-15, from 0.13% of GDP to 0.06% in the recent Budget.
• The Mid-Day Meal (MDM) programme, covering 12 crore children, also experienced a significant drop in funds as a share of GDP since 2014-15, despite its success in improving educational and nutritional outcomes.
• Primary and secondary education expenditures also declined as a share of GDP, from 0.25% last year to 0.22% this year, raising concerns about education quality and infrastructure.

Health Sector: A Slight Increase

• The only positive development in the Budget was a slight increase in the allocation for health, with the Health Ministry’s share rising from 0.25% of GDP in 2014-15 to 0.28% this year.
• However, this increase is insufficient given the high out-of-pocket expenditure on health that pushes millions into poverty every year.

Overall Decline in Welfare Spending

• The Budget allocation for the mentioned schemes and departments has decreased from 2.1% of GDP in 2014-15 to 1.53% this year.
• In the COVID-19 pandemic year of 2020-21, this allocation was 4.31% of GDP, highlighting the essential role these schemes play during crises.

Impact of Corporate Tax Cuts

• The government has reportedly forgone over ₹8 lakh crore in tax revenue since slashing corporate tax rates in 2019, reducing fiscal space and sacrificing the welfare of the poor and vulnerable.
• This financial strategy is linked to India’s poor Human Development Index rank of 132 and growing inequality, as highlighted by a recent World Inequality Lab report.

Conclusion

• The Union Budget’s diminished focus on welfare schemes risks exacerbating poverty and inequality in India, undermining efforts to create a truly developed society.
• To ensure inclusive growth, the government must prioritise social welfare, invest in critical schemes, and address the needs of the most vulnerable populations.

Practice Question:  Discuss the implications of reduced budget allocations for welfare schemes like MGNREGA and NFSA on poverty alleviation and social equity in India. How can the government balance fiscal discipline with the need for social welfare? (150 Words /10 marks)

2. Reshape the governance structures of AI companies

(Source – The Hindu, International Edition – Page No. – 8)

Topic: GS2 – Governance

Context

The article discusses the tension between profit-making and social responsibility in corporate governance, particularly in AI development. It explores challenges like privacy concerns and algorithmic biases, examining how companies like OpenAI and Anthropic are adopting alternative governance structures while questioning the sustainability of these models in a profit-driven market.

Shareholder Primacy vs. Stakeholder Approach

• Traditional corporate governance has been dominated by the theory of shareholder primacy, where the main objective of businesses is to generate profit and create wealth for shareholders.
• There is a contrasting stakeholder benefit approach that aims to maximise the benefits for all stakeholders, not just shareholders.
• Recently, stakeholder capitalism has gained traction, with corporations increasingly focusing on social objectives alongside profit-making, particularly in sectors like Generative Artificial Intelligence (AI).

Challenges in AI Development: Data Access Issues

• Developing AI technologies requires access to vast amounts of data, which raises privacy concerns.
  • Example: Meta was asked to pause its AI model training in Europe due to concerns raised by the Irish privacy regulator about using public content from Facebook and Instagram.
• Algorithmic biases are another significant concern, where human prejudices can be embedded into AI systems.
  • Example: Amazon discontinued a recruiting algorithm after discovering gender bias, and a Princeton University experiment highlighted racial biases in AI’s word associations.

Corporate Responses to AI Challenges: Governance Structures

• To mitigate the risks associated with AI, some companies have altered their corporate governance structures to prioritise responsible AI development.
  • Example: Anthropic has a governance structure called the Long-Term Benefit Trust, composed of financially disinterested members who can influence board decisions.
  • OpenAI initially started as a non-profit but later transitioned to a hybrid model with a capped profit-subsidiary to support its capital-intensive innovation.

Clash Between Purpose and Profit: The OpenAI Debacle

• Even companies with alternative governance models face challenges when balancing social purpose with profit-making.
  • Example: OpenAI experienced a governance crisis when its non-profit board fired CEO Sam Altman over concerns of rapid AI commercialization compromising user safety. This move was opposed by Microsoft and most employees, leading to Altman’s reinstatement and the board’s replacement.
• This incident has led to questions about the viability of public benefit corporations in the tech industry, where financial backing from shareholders is crucial.

Friedman’s Perspective: Profit Over Social Responsibility

• In 1970, Milton Friedman argued that a business’s primary social responsibility is to generate profits for its shareholders.
• The recent events in companies like OpenAI suggest that public benefit structures might merely disguise profit-seeking motives rather than genuinely prioritising social interest.

Strategic Solutions: Balancing Profit and Social Purpose

• The current accountability structures, which include appointing independent boards and adopting social benefit objectives, are insufficient to protect against the amoral drift towards profit-driven goals.
• Policymakers must find innovative regulatory methods that balance the conflicting interests in AI development.
  • Three key areas to target include:
    • Enhancing long-term profit gains from adopting a public benefit purpose.
    • Incentivizing managerial compliance with such purposes.
    • Reducing compliance costs associated with these purposes.
• This approach would involve framing ethical standards for AI governance and providing regulatory backing through corporate governance reforms.

Conclusion:

• With the increasing involvement of AI in various aspects of life, it is critical to adopt governance models that promote the ethical development of AI while also generating profits.
• Effective governance in AI requires a delicate balance between social responsibility and profit-making, supported by innovative and regulatory frameworks.

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 challenges and implications of balancing profit-making with social responsibility in the development of AI technologies, with reference to recent corporate governance trends. (250 Words /15 marks)