Model Answer

Introduction

The statement, “The concept of Just and Unjust is contextual,” highlights the dynamic nature of justice, which evolves with societal values and norms. As John Rawls noted, justice is essential for social institutions, and what was deemed just in the past may now be perceived as unjust. This evolution necessitates constant scrutiny to prevent miscarriages of justice.

Historical Legislation and Social Evolution

Justice has historically evolved in response to changing societal norms. The Bengal Sati Regulation of 1829 outlawed the practice of Sati, illustrating how cultural practices once accepted as just were redefined as unjust through legal reform.

Technological Advances and Legal Responses

As technology advances, laws must adapt to new realities. The Information Technology Act of 2000, amended in 2008, reflects a response to digital crimes, ensuring data privacy and curbing cybercrime, thereby applying utilitarian ethics for the greater good.

Economic Changes and Justice

Economic shifts often require a reevaluation of justice. After India’s liberalization in the 1990s, labor laws were revised to ensure fairness and equity in a new capitalist framework, grounded in Rawlsian principles of distributive justice.

Shifts in Collective Morality

Changing societal values lead to legal reforms. The 2018 Supreme Court ruling decriminalizing homosexuality in India overturned colonial-era laws, reflecting evolving attitudes towards LGBTQ+ rights.

Public Health and Ethics

During the COVID-19 pandemic, lockdowns exemplified the need to balance individual freedoms with collective health, requiring a redefinition of justice in public health contexts.

Continuous Scrutiny of Justice

To prevent miscarriages of justice, several measures are essential:

1. Regular Legal Reforms: Continuous updates to laws ensure relevance. The Indian Penal Code has been revised to address cyberbullying.
2. Judicial Reviews: The Supreme Court uses judicial review to align laws with evolving social values, safeguarding fundamental rights.
3. Ethical Committees: In emerging fields like AI, ethics committees evaluate implications to ensure fairness and prevent injustices.
4. International Benchmarking: Aligning national laws with international standards helps prevent normative isolationism and ensures global justice.
5. Educational Reforms: Law schools now offer courses in cyber law, equipping future jurists to address contemporary challenges.

Conclusion

Justice is not static; it must evolve with societal, technological, and moral changes. Constant scrutiny and adaptation of legal frameworks are essential to uphold fairness and equity. As Dr. B.R. Ambedkar emphasized, justice is about rendering every person their due, requiring continuous reflection and reform in our understanding of what is just.

Model Answer

Introduction

The integration of Artificial Intelligence (AI) into administrative decision-making has sparked significant ethical debates. While AI promises efficiency and objectivity, its application raises concerns regarding transparency, accountability, and bias.

Positive Aspects of AI

1. Data-Driven Efficiency: AI processes vast data sets quickly, leading to informed decisions. For instance, Singapore’s AI-driven urban planning enhances traffic management (Source: Singapore Urban Redevelopment Authority).
2. Cost Reduction: Automation of routine tasks through AI can significantly lower administrative costs. AI chatbots in e-governance exemplify this reduction (Source: Government of India).
3. Predictive Analytics: AI can forecast trends, aiding proactive decision-making, as seen during the COVID-19 pandemic when models predicted virus spread (Source: World Health Organization).
4. Consistency: AI ensures uniformity in public resource allocation, evident in direct benefit transfer schemes (Source: NITI Aayog).
5. Reducing Judicial Bias: AI tools like SUPACE assist in legal research, potentially minimizing human biases (Source: Supreme Court of India).

Ethical Concerns

1. Algorithmic Bias: AI can perpetuate existing biases if trained on flawed data. Predictive policing has faced criticism for disproportionately targeting minorities (Source: ProPublica).
2. Lack of Accountability: Automated decisions may escape accountability. For example, erroneous welfare decisions in the UK highlight this issue (Source: BBC News).
3. Ethical Dilemmas: AI struggles with moral complexity, particularly in healthcare, where critical patient prioritization raises ethical questions (Source: The Lancet).
4. Dependence on Technology: Overreliance on AI could diminish human oversight, leading to vulnerabilities (Source: Cybersecurity and Infrastructure Security Agency).
5. Job Displacement: Automation threatens jobs in administrative sectors, exacerbating socio-economic inequalities (Source: International Labour Organization).

Conclusion

The ethical implications of AI in administrative decision-making are complex. While AI can enhance efficiency and consistency, it poses significant risks related to bias, accountability, and human oversight. A balanced approach, emphasizing ethical standards and human involvement, is vital to ensure AI serves public welfare without compromising rights.