Artificial intelligence (AI) is changing our lives faster than most societies can adapt. It brings real promise — better public services, earlier warning for crises, new opportunities for entrepreneurs — but also real fear: loss of control, deeper inequality, and a world where data becomes the new currency. For the Global South, AI is not a threat by nature. It can be a historic opportunity to accelerate inclusion, strengthen social justice, and expand women’s leadership. Yet this opportunity unfolds at a fragile moment for democracy and participation. CIVICUS’ global monitoring shows how acute the pressure is on civic freedoms, which are the space people have to speak, organise, and influence decisions. When civic space shrinks, technology can either widen it or close it further.

Data: the hidden driver of inclusion

There is a simple truth often missed in AI debates: AI is only as good as the data it learns from. Where data is scarce, outdated, biased, or concentrated in a few capital cities and large institutions, AI will reproduce the same blind spots, and sometimes amplify them.

This is why the data question is also an inclusion question. If rural regions, underserved neighbourhoods, informal economies, and women’s lived realities are missing from data, they will be missing from AI-driven decisions. In practice, the risk is clear: public services optimised for those already visible, credit systems built on incomplete histories, education tools that ignore local languages, and risk models that overlook the most vulnerable.

So the key question becomes: How can the Global South build data ecosystems that make AI work for everyone, including women and marginalised regions?

Women, civic space, and the risk of “automated exclusion”

Women in the Global South face intersecting barriers: limited digital access, economic precarity, constraints in education and finance, restricted mobility, and persistent gender-based violence. UN Women also highlights how technology-facilitated abuse can silence women’s voices in the public sphere, precisely where civic participation should be expanding.

If we are not careful, AI can multiply these inequalities: automated discrimination in hiring or credit, opaque targeting in service delivery, harassment amplified by synthetic media, and new forms of surveillance that discourage participation. When civic space is already under strain, these dynamics can erode trust and deepen disengagement.

But the opposite is also possible. Used responsibly, AI can help close gaps in:

• Education: personalised learning for girls in multilingual and underserved settings

• Health: stronger maternal care, early detection, support for women community health workers

• Economic empowerment: fairer access to finance and market intelligence for women entrepreneurs

• Protection and rights: better mapping of violence, faster referral to services, improved prevention

The difference lies in governance and in data: who collects it, who owns it, who can access it, and who can challenge decisions made from it.

Building “local data” as a public good

For inclusive AI, the priority is not simply more data — it is better data governance. Countries and communities need data systems that are:

1. Representative: Data must reflect the diversity of society, including its regions, languages, informal work, and gender realities, not only what is easiest to measure.

2. Safe and rights-based: Inclusion cannot come at the cost of privacy or safety. Data protection, informed consent, and safeguards against misuse are essential, especially for women and human rights defenders.

3. Interoperable and usable: Data that cannot be shared responsibly across institutions is rarely actionable. Common standards and clear stewardship matter as much as collection.

4. Action-oriented: Data should improve decisions, not just fill reports. The most effective reforms often rely on short learning cycles — test, measure, adjust — rather than one-off “perfect plans.”

This is where public policy monitoring becomes a powerful bridge between data and democracy: it turns information into accountability, and accountability into trust.

Why policy observatories matter: the OTPP example

In Tunisia, public policy monitoring tools such as the Observatoire Tunisien des Politiques Publiques (OTPP) illustrate how local data can strengthen delivery and inclusion. By tracking public investment and policy implementation, such platforms help make local realities visible: what is being delivered, where gaps persist, and which regions are left behind.

This matters for AI for a practical reason: AI systems need structured, reliable, local data to be effective. And it matters for a democratic reason: when data is shared through transparent indicators, citizens, civil society, and local actors can engage with policy based on evidence — not rumors, not opacity.

In other words, local data is not only fuel for AI. It is also a lever for regional inclusion, accountability, and civic trust. The governance foundations already exist; we must implement them. The global benchmarks are clear:

• UNESCO’s Recommendation on the Ethics of AI places human rights and dignity at the centre and calls for transparency, fairness, and human oversight.

• The UN High-level Advisory Body’s report, Governing AI for Humanity, argues for inclusive governance, risk management, and capacity-building so benefits are shared and harms contained.

• The Global Digital Compact frames digital cooperation around inclusion, rights, and trust — foundations for equitable AI.

The implementation challenge is where the Global South must lead with clarity. A practical path could include:

1. Prioritise data readiness as much as AI readiness. UNESCO’s Readiness Assessment Methodology (RAM) can help countries assess their overall preparedness. But readiness must also mean data: legal frameworks, stewardship, protection, and the ability to generate local datasets ethically.

2. Make women and local regions co-authors of the data agenda. Inclusion is not a communications strategy. It requires decision-making power: in data governance bodies, procurement committees, audit teams, and national strategies.

3. Invest in local data infrastructures. Connectivity and skills remain essential — but so do the “quiet” investments: registries, interoperable systems, quality control, training for public servants, and mechanisms to publish simple indicators.

4. Use procurement to demand inclusive data and audits. Governments can require impact assessments, bias testing, local language support, accessibility, and evidence that systems work outside capital cities.

An augmented humanism — led by women, grounded in local realities

The real question is not whether AI will replace humans, but whether it will help us decide better and live better together. Women in the Global South carry leadership forged in contexts of crisis, care, and collective action. If AI is governed with responsibility, it can amplify these strengths, but only if it is built on data that reflects the full society, including its regions and its invisible work.

AI will not make the world more equitable on its own. But inclusive, rights-based data ecosystems can ensure that AI serves what matters most: participation, dignity, and shared prosperity.

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