The Human in the Loop: Why the Future of AI is a Matter of Equity

A conversation with Dr. Ronda Železný-Green - CEO of datocracy AI, Chief Learning Officer at BRK Ujima, and Co-Founder of Panoply Digital - on bridging the gap between frontier AI capabilities, educational technology, and the empowerment of the Global Majority.

By Louise Servoin · 2026-10-29 · 10 min read

## Dr. Ronda Železný-Green – CEO of datocracy AI, Chief Learning Officer at BRK Ujima, and Co-Founder of Panoply Digital

Expert in AI upskilling, EdTech, and digital safeguarding. Ph.D. in Human Geography with an ICT4D specialism from Royal Holloway, University of London, and Google Certified Generative AI Leader.

A practitioner-scholar bridging the gap between complex data architectures and human-centered social justice, she has led AI transformation strategies across the globe. Based in London, United Kingdom.

Some professionals understand the algorithms. Others understand the socio-cultural realities of the communities impacted by them. Rarely do you find someone who navigates both while operating at the high-stakes intersection of international development and commercial AI implementation. Dr. Ronda Železný-Green’s trajectory, from foundational work in digital inclusion and mobile learning in sub-Saharan Africa to designing AI and data upskilling programmes adopted by governments and global education networks, places her at a unique nexus. She operates as the critical "human in the loop," ensuring that populations historically marginalized by the digital divide are not left behind in the generative AI era.

We sat down with Dr. Železný-Green to discuss why effective AI adoption starts with real organisational problems rather than tech hype, the importance of Small Language Models (SLMs) in global education, and why the future of AI must be shaped by the Global Majority.

## The Pragmatic Implementer

Dr. Železný-Green, you have an extensive background in both academic research and high-level strategy for organizations like the GSMA, the Internet Society, and data.org. When you advise massive networks on AI transformation, how do you bridge the gap between theoretical AI capabilities and on-the-ground adoption?

It comes down to starting with the problem, not the technology. Many organizations get enamored with the idea of artificial intelligence, but tool-based training simply doesn't stick. My approach is to start every single engagement with one fundamental question: "What does success look like in 90 days?"

If you are a civil servant in Ghana or a legal team member, you don't just need to understand what a Large Language Model is; you need outcome-based upskilling. The training has to be focused on practical realities, like being able to deliver a campaign brief using AI by Monday. Through Panoply Digital's partnership with the Tony Blair Institute on the TBI Digital Academy, we used this exact outcome-based approach with public servants in Ghana, Malawi and Senegal, helping them genuinely change their daily workflows rather than just demonstrating what AI can hypothetically do.

The same logic drove the Responsible Data Management course I led at data.org, which was adopted by the governments of Nigeria and India to upskill over 3.1 million civil servants in ethical data governance.

## The EdTech Evolution

You've worked on AI upskilling within networks like Teach For All, which spans more than 60 partner organizations across six continents. How do you deploy AI effectively in such diverse and often resource-constrained environments?

The key is recognizing that massive, generalized Large Language Models aren't always the right fit for every educational context. Teach For All's partnership with Anthropic, launched through the AI Literacy & Creator Collective, now reaches educators across 63 countries — and what makes it work is that teachers are treated as co-architects rather than end users. But beyond partnerships of that scale, I heavily emphasize the use of Small Language Models (SLMs) and purpose-built, lightweight assistants.

As a practitioner working hands-on with Small Language Models, I oversaw the development and deployment of custom AI workplace chatbots—which we called "GEMS"—specifically tailored for Girls' Education programming. By embedding these tools into daily administrative tasks using Retrieval-Augmented Generation (RAG) pipelines and Agentic Workflows, we made the technology practically useful. Innovation in EdTech isn't about throwing the most computationally heavy model at a school; it's about building solutions that seamlessly integrate into the daily realities of educators and program managers.

## Digital Safeguarding and Equity

Through your work as Chief Learning Officer at BRK Ujima and as a researcher, you’ve tackled some of the darker sides of digital innovation, specifically regarding marginalized communities. How is generative AI exacerbating these issues, and what can be done?

Generative AI, like any technology, acts as an amplifier of existing societal structures. At BRK Ujima, working in partnership with the charity Glitch, we have conducted extensive research into "digital misogynoir"—the specific intersection of anti-Black racism and misogyny experienced by Black women online.

With the rise of generative AI, we are seeing these harms supercharged through deepfakes and AI-driven online gender-based violence (OGBV). These technologies can replicate and automate discrimination. My work focuses on providing the empirical baseline needed to understand these intersections of race and gender with deepfake tech, aiming to create non-criminal redress schemes for victim-survivors and to ensure that digital safeguarding strategies are built into the core of tech deployment. We cannot build an equitable digital future if we do not intentionally design systems to protect the most vulnerable users.

## The Question of the Future

As the CEO of datocracy AI, you are leading a movement to reclaim the democratization of technology. What is the ultimate goal for the Global Majority in the AI era?

The goal is to ensure that the future of data and AI is fundamentally shaped by the Global Majority—meaning we shift the locus of control and credentialing away from traditional gatekeepers. Through the datocracy Charterhood, we are building a global network of credentialed, ethical AI practitioners, engaging learners across Africa, the Americas, and India in AI governance and data literacy.

Ultimately, my philosophy regarding AI is very simple, and it applies whether I'm advising multilateral organizations or grassroots feminist groups: I don't replace people with AI. I make people irreplaceable with it. The technology is the engine, but human judgment, lived experience, and cultural nuance must remain the driver.

"I don't replace people with AI. I make people irreplaceable with it." — Dr. Ronda Železný-Green

## The Golden Thread

We often see a gap between what technology can do and what professionals actually adopt. How do we ensure AI becomes a 'co-pilot' that elevates human judgment rather than a tool that encourages intellectual passivity?

The difference between a co-pilot and a crutch comes down to one thing: intention at the point of design.

When AI is introduced through the lens of real problems, with real people treated as architects of their own workflows rather than passive recipients of someone else's solution, it sharpens human judgment rather than softening it. Practitioners who build with AI learn to question it. They learn where it fails, what it cannot see, and what only they can bring to the work.

But this only holds if equity is built in from the start. The communities with the most at stake in the AI era are often those given the least agency within it. Handing powerful tools to people without investing in their capacity to interrogate those tools is not empowerment. It is dependency by another name.

The answer is not slower adoption. It is more intentional adoption: grounded in outcomes, led by the people closest to the problem, and designed to make human experience more visible, not less. When AI is built around people rather than handed down to them, intellectual passivity does not get a foothold.

Tags: AI Transformation, EdTech, Digital Equity, Global Majority, datocracy AI, Gender & Tech, AI Upskilling