AI Fairness and Social Impact

Global Program on Equity-Centered AI Design, Deployment, and Governance

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

“AI Fairness and Social Impact” is an interdisciplinary, impact-driven program that addresses the urgent need to design and deploy AI systems that do not exacerbate bias, discrimination, or inequality, especially in critical areas such as healthcare, finance, education, policing, and welfare services.

Participants will explore socio-technical frameworks for fairness, ethics, and accountability, learn to apply bias mitigation methods, evaluate disparate impact and equity trade-offs, and analyze real-world harms caused by opaque or poorly designed AI systems. The course also examines AI’s influence on labor markets, public discourse, and democratic institutions.

Aim: To empower participants to create and deploy AI systems that are fair, inclusive, socially responsible, and aligned with the principles of equity, transparency, and public interest.

Program Objectives:

Raise awareness of the societal implications of algorithmic decision-making
Promote inclusive AI development practices rooted in justice and ethics
Equip participants to assess and mitigate bias using real-world tools
Empower civic, legal, and technical collaboration on AI accountability
Build public trust in AI by advancing explainability, inclusivity, and reparative design

What you will learn?

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

Intended For :

AI/ML practitioners and data scientists
Policy professionals and regulators
Legal experts and ethicists
Academics and researchers in sociology, STS, and AI ethics
NGO and civil society actors working in tech justice