Diversity, Equity, and Inclusion (DEI) in the Age of AI

Ensure GenAI systems promote fairness and avoid biased outcomes.

Pillar

People – Mindset, Leadership & Change

Overview

This course focuses on the critical importance of embedding Diversity, Equity, and Inclusion principles within the design, deployment, and management of Generative AI systems. Participants will learn how to identify and mitigate bias, foster inclusive AI practices, and promote equitable outcomes in AI-powered solutions.

Learning Objectives

Participants will be able to:

• Understand sources and impacts of bias in Generative AI systems

• Recognize the importance of DEI in AI design and implementation

• Apply strategies to reduce bias and ensure fairness in AI outputs

• Develop inclusive AI governance and ethical frameworks

• Advocate for equitable AI adoption within their organizations

Target Audience

• AI developers and data scientists

• Business leaders and AI project managers

• DEI officers and HR professionals

• Policy makers and ethics committees

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures and case studies

• Hands-on bias detection exercises

• Group discussions and policy design workshops

Materials Provided

• DEI and AI Ethics Toolkit

• Guidelines for Fair and Inclusive AI

• Bias assessment templates and checklists

• Certificate of completion

Outcomes

• Identify and mitigate bias in GenAI applications

• Build AI systems that reflect diverse perspectives and needs

• Implement governance that supports equitable AI use

• Champion DEI values throughout AI initiatives

Outline / Content

Day 1: Foundations of DEI in AI

• Introduction to Diversity, Equity, and Inclusion concepts

• How bias emerges in AI and its societal impact

• Real-world examples of AI bias and lessons learned

Day 2: Detecting and Mitigating AI Bias

• Tools and techniques for bias identification

• Approaches to reducing bias in data and models

• Inclusive design principles for AI development

Day 3: Governance and Ethical Frameworks for DEI

• Building policies for fair AI usage

• Accountability and transparency in AI systems

• Creating inclusive AI teams and workflows

Day 4: Advocating and Scaling DEI in AI Adoption

• Communicating DEI value to stakeholders

• Embedding DEI in organizational AI strategies

• Final workshop: Designing a DEI action plan for AI projects