AI Fairness and Social Impact
Global Program on Equity-Centered AI Design, Deployment, and Governance
Early access to e-LMS included
About This Course
“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
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?
- 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
Program Outcomes
- Evaluate AI systems for bias, fairness, and disparate outcomes
- Apply fairness metrics such as demographic parity, equal opportunity, and calibration
- Build socially responsible, equity-aligned AI pipelines
- Align technical work with human rights and anti-discrimination laws
- Receive a professional certificate in “AI Fairness and Social Impact”
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →