policy in the age of artificial intelligence
Course Objective
Artificial Intelligence is no longer a futuristic concept; it is actively reshaping economies, societies, and the very practice of governance. From service delivery and predictive analytics to regulatory challenges and ethical dilemmas, policymakers must be equipped to harness AI’s potential while mitigating its risks. This course provides a comprehensive framework for understanding, analyzing, and designing public policy for an AI-driven world, with a special focus on the Indian context.
Target Audience
Mid to senior-level government officials, public policy professionals, legal experts, technology planners, civil society leaders, and researchers aiming to understand and shape the future of governance
Ai Fundamentals
Analyze how AI is transforming the public policy cycle, from agenda-setting to evaluation.
Identify opportunities for leveraging AI to improve public service delivery, especially in sectors relevant to India like agriculture, health, and education.
Understand the key principles of AI governance and analyze different global regulatory approaches.
Develop frameworks for creating responsible, fair, and transparent AI policies.
Design a Policy AI Lab concept for solving a real-world public problem relevant to the local or national context.
Communicate complex AI-related policy issues to diverse stakeholders effectively.
Week 1: Foundations
- AI in the Public Sphere: Moving beyond the hype. Real-world examples of AI in governance globally.
- The Data Imperative: Understanding the role of data as the fuel for AI; concepts of Big Data, Open Data, and data quality.
- Demystifying AI: What is it and what it is not? Core concepts of Machine Learning (ML), Natural Language Processing (NLP), and Generative AI (e.g., LLMs).
- Agenda Setting: Using predictive analytics and data mining to identify emerging social and economic issues.
- Policy Formulation: AI for modeling and simulating policy outcomes.
- Implementation: Automating government processes, personalized service delivery (e.g., targeted welfare).
- Evaluation: Real-time monitoring and impact assessment using AI tools.
- India’s National Strategy for AI.
- Role of NITI Aayog, MeitY, and state-level AI initiatives.
- Key government initiatives: IndiaAI Mission, Digital India, Aadhaar, and the India Stack as enablers.
- Participants will work in groups to identify and frame a public policy problem in India that could potentially be addressed using AI.
Week 2
- Healthcare: AI in diagnostics, Personalized medicine, public health surveillance (e.g., predicting disease outbreaks), and hospital management.
- Education: Personalized learning platforms, AI for teacher assistance, and improving administrative efficiency.
- Agriculture: Precision farming, crop yield prediction, soil health monitoring, and supply chain optimization.
- Smart Cities & Rural Infrastructure: AI in traffic management, energy efficiency, waste management, and planning rural connectivity.
- Environment & Disaster Management: AI for climate modeling, forest fire prediction, and early warning systems for landslides and floods (highly relevant for Uttarakhand and Himachal Pradesh).
- AI in national security
- Ethical challenges of predictive policing and surveillance.
- Applications in the justice system (e.g., case management, legal research).
An interactive session with a leader from a GovTech startup or a state IT department implementing AI projects, discussing practical successes and failures.
Week 3
- How AI can inherit and amplify human biases (gender, caste, race).
- Case studies of biased algorithms in hiring, loan approvals, and social welfare.
- Technical and policy strategies for promoting fairness and mitigating bias.
- The “black box” problem: Why is it hard to understand AI decisions?
- The importance of Explainable AI (XAI) in public policy.
- Defining accountability: Who is responsible when an AI system fails?
- Deep dive into the Digital Personal Data Protection (DPDP) Act, 2023
- Rights of the “Data Principal” and obligations of the “Data Fiduciary.”
- Implications for government projects using citizen data.
- Comparative analysis: The EU’s risk-based AI Act, the US’s market-driven approach, China’s state-centric model
- Discussing the path forward for “India’s Way” in AI governance – balancing innovation with regulation
- A structured debate on a topic like: “The use of facial recognition technology for public security in India should be broadly expanded.”
Week 4
- This is the central, hands-on component of the course. Instead of a traditional field visit, participants will bring the “field” to the lab.
- Objective: Participants, in their groups from Week 1, will design a comprehensive AI-powered policy intervention for the problem they identified.
- Refine Problem Statement: Clearly define the policy goal.
- Solution Design: Conceptualize an AI system (no coding required). What would it do?
- Data Strategy: What data is needed? Where will it come from? How will it be governed under the DPDP Act
- Implementation Roadmap: Key stakeholders, required capacity, and pilot phase design.
- Ethical Guardrails: How will you address potential bias, ensure transparency, and establish accountability
- Faculty and mentors will provide intensive guidance throughout this process. A problem relevant to Uttarakhand (e.g., “AI for promoting sustainable tourism”) would be highly encouraged.
- How to write effective policy briefs on complex tech issues.
- Strategies for communicating with political leaders, the media, and the public about AI, avoiding both hype and fear-mongering.
- Groups present their “Policy Lab” proposals to a panel of instructors and guest experts.
- The focus will be on the robustness of the policy design, feasibility, and ethical considerations.
- Building an agile and adaptive mindset for lifelong learning.
- Course wrap-up, feedback, and certificate distribution.
- Looking ahead: The impact of Artificial General Intelligence (AGI), quantum computing, and other emerging technologies on policy.
Resource Provided
- Faculty: A mix of public policy experts, AI technologists/data scientists, and legal/ethical scholars. Guest Speakers: Officials from MeitY/NITI Aayog, state-level IT/AI missions, founders of Indian AI startups, and civil society advocates working on digital rights.
- Tech Infrastructure: Robust internet, access to online collaboration tools, and potentially access to some cloud-based AI platforms for demonstration purposes.
- Reading Materials: Curated list of white papers (e.g., from NITI Aayog), academic articles, international reports (OECD, UNESCO on AI ethics), and case studies.
- Residential Facilities: Comfortable accommodation and collaborative working spaces in Dehradun
Assessment & Evaluation
- Workshop Deliverables: Submission of policy brief outlines. [30 Marks]
- Active Participation: Engagement in discussion, group activities and Q&A. [20 Marks]
- Peer Feedback: Participants provide constructive feedback on each others’ presentations [20 Marks]
- Final Group Presentation: A comprehensive presentation of policy brief incorporating formulation and implementation strategies, drawing insights from fieldwork. [30 marks]
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