The AI Divide: Who Gets Included, Who Gets Left Behind, and What We Do About It

​Event Description

AI is reshaping economies, institutions, and access to opportunity, but much of this transformation is unfolding far from the realities of informal economies, where most people actually live and work. The risk is not just a digital divide, but a deeper AI divide, where systems are built in ways that exclude entire populations by design.

This session explores what it takes to make AI inclusive, usable, and equitable in low-resource, multilingual, and informal contexts. How do we design systems that reflect how people actually earn, communicate, and make decisions? And how do we ensure that access to AI translates into real economic opportunity, not just exposure to new tools?

We will also examine how bias is embedded in AI systems, shaping outcomes in employment, finance, health, and justice, and what it takes to identify and address these inequities. At a structural level, the conversation will look at the urgent need to invest in the governance, data infrastructure, and operational capacity of smaller, community-based, and underrepresented organizations, without which the benefits of AI will remain concentrated among the already powerful.

Designed as a practical and candid exchange, this session moves beyond surface-level discussions to focus on how we build an AI future that includes the majority, not just the privileged few.

Speakers

• ​Katerina Chantzi is a Social Business and Organisational Development Consultant, advancing purpose-driven transformation through trust, collaboration, and impact.

• ​Liz Robinson is CEO of Charmaghz, advancing access to education for Afghan girls and young women through online learning.

• ​Rachel Mannino is Founder and CEO of CharityScribe, advancing ethical AI adoption to strengthen fundraising and operations in the social sector.

• ​Adela Andrea Gonzalez Pacheco is CEO of Nuestro Flow, advancing diversity, equity, and inclusion across Latin America through consulting and social impact initiatives.

​Discussion Questions
What does it mean to design AI for informal, low-resource, and multilingual contexts?
How are biases in AI systems created, and how do they shape real-world outcomes?
What investments are needed to ensure smaller and underrepresented organizations can participate in the AI ecosystem?
How do we move from AI access to meaningful economic opportunity and inclusion?