Ethics Theory, TLP-UPSC Mains Answer Writing
Q. 5. The increasing role of Artificial Intelligence in governance poses new ethical dilemmas. Discuss the challenges of ensuring transparency, fairness, and accountability in AI-driven public decision-making. (150 words, 10 marks)
Introduction
Artificial Intelligence (AI) is increasingly deployed in public governance for tasks like predictive policing, welfare delivery, and regulatory enforcement. While it enhances efficiency, it raises critical ethical questions around transparency, fairness, and accountability.
Body
Ethical Challenges in AI-Driven Governance
The integration of AI into public systems amplifies existing administrative concerns while adding new dimensions of complexity and risk.
- Opacity of Algorithms: Many AI models function as “black boxes,” making it difficult to understand how decisions are made.
Example: AI-based loan approval systems have denied benefits without applicants knowing the reason. - Bias and Discrimination: AI systems often replicate or amplify existing social biases present in training data.
Example: COMPAS algorithm in the U.S. was found to assign higher recidivism risk scores to Black defendants. - Lack of Accountability: Diffused responsibility between developers and administrators makes redress difficult.
Example: Facial recognition errors in public surveillance leading to wrongful arrests. - Data Privacy Concerns: AI systems require large datasets, potentially infringing on citizen privacy.
- Automation Bias: Officials may over-rely on AI recommendations, reducing critical oversight.
- Exclusion of Stakeholders: Marginalized communities are often not involved in designing AI systems that impact them.
Ensuring Ethical Use of AI in Governance
To make AI systems more aligned with democratic and ethical values, institutional and technical safeguards must be employed.
- Algorithmic Transparency: Make AI systems auditable and explainable to the public.
Example: EU’s AI Act mandates high-risk systems be interpretable and documented. - Fairness by Design: Integrate bias detection and correction in the development stage.
Example: IBM’s Fairness 360 toolkit helps assess algorithmic discrimination. - Clear Accountability Norms: Define who is responsible for each stage of AI implementation and its consequences.
- Data Governance: Strengthen consent, anonymization, and data minimization protocols. (Incorporate Sri Krishna committee recommendations)
- Human-in-the-Loop Mechanism: Retain human oversight in critical decision-making processes.
Example: Delhi Police’s use of AI tools includes manual verification layers for final action. - Inclusive Design Processes: Involve diverse stakeholders in AI system design to reflect public interest.
Conclusion
As per UNESCO’s 2021 Recommendation on the Ethics of AI, nations must ensure that AI development respects human rights, transparency, and accountability. Ethical AI in governance isn’t option
