AI Fairness and Social Impact

Aim

Program Objectives

Program Structure

Week 1: Foundations of Fairness in AI
Module 1: Understanding AI Fairness

• Chapter 1.1: What is Fairness in AI? Definitions and Context
• Chapter 1.2: Discrimination, Equity, and Power in Algorithmic Systems
• Chapter 1.3: Key Concepts: Group Fairness, Individual Fairness, Procedural Fairness
• Chapter 1.4: Historical Case Studies of Bias and Harm in AI Deployment

Module 2: Metrics, Trade-offs, and Tensions

• Chapter 2.1: Popular Fairness Metrics and When to Use Them
• Chapter 2.2: Trade-offs Between Accuracy, Fairness, and Utility
• Chapter 2.3: Technical vs. Contextual Fairness
• Chapter 2.4: Critical Perspectives: Fairwashing, Audit Theatre, and Incomplete Solutions

Week 2: Social Impacts of AI Across Sectors
Module 3: AI in High-Stakes Domains

• Chapter 3.1: Criminal Justice Algorithms and Racial Disparities
• Chapter 3.2: AI in Health, Education, and Public Benefits
• Chapter 3.3: Labor Market Impacts and Discrimination in Hiring Tools
• Chapter 3.4: Surveillance, Policing, and AI at the Margins

Module 4: Community Engagement and Participatory AI

• Chapter 4.1: Who Gets to Define Fairness? Power and Representation
• Chapter 4.2: Case Studies of Participatory AI Design
• Chapter 4.3: Impact Assessments and Community Consultations
• Chapter 4.4: Building Culturally Responsive AI Systems

Week 3: Ethics, Governance, and Change-Making
Module 5: AI Governance for Fairness

• Chapter 5.1: Policy Responses and Legislative Proposals (EU AI Act, Algorithmic Accountability Act, etc.)
• Chapter 5.2: Organizational Governance: AI Ethics Boards and Equity Audits
• Chapter 5.3: Transparency, Documentation, and Accountability Mechanisms
• Chapter 5.4: Interventions for Equitable AI: Redesign, Rejection, Regulation

Module 6: Capstone & Social Impact Strategy

• Chapter 6.1: Evaluating Real-World AI Systems for Fairness and Harm
• Chapter 6.2: Capstone Project: Social Impact Assessment of an AI System
• Chapter 6.3: Presenting Findings to Stakeholders (Public, Technical, and Policy)
• Chapter 6.4: Leadership for AI Justice: Advocacy, Allyship, and Institutional Change

Who Should Enrol?

Program Outcomes

Fee Structure

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