AI and the Global South: Why Civil Society Must Be Central to AI Decision-Making

Introduction: AI Is Transforming Development—But Governance Is Lagging

Artificial intelligence is rapidly reshaping how international development and humanitarian organizations operate. From predictive analytics for food insecurity to automated beneficiary targeting and AI-assisted reporting, these tools are increasingly embedded in program design, implementation, and evaluation.

For monitoring and evaluation (M&E) professionals, AI and the Global South presents clear advantages: faster data processing, new sources of evidence, real-time insights, and the potential to improve learning and decision-making. However, alongside these opportunities comes a growing concern: AI is being adopted faster than the governance systems needed to ensure it is ethical, inclusive, and accountable.

Nowhere is this tension more visible than in the Global South. While many AI-driven interventions are deployed in low- and middle-income countries, the decisions shaping these systems—what problems they solve, what data they use, and how risks are managed—are often made elsewhere. This raises a fundamental question for development practitioners: Can AI meaningfully support development outcomes if civil society and affected communities are excluded from AI decision-making?

AI in Humanitarian and Development Practice: From Promise to Practice

AI is often framed as a solution to mounting pressures in the development and humanitarian sectors: rising needs, shrinking budgets, complex crises, and increasing accountability demands. In practice, AI is already being used to:

From an operational perspective, these applications can significantly increase efficiency. For evaluators, they open new methodological possibilities like real-time monitoring and pattern detection. However, efficiency should not be confused with effectiveness. Civil society organizations working at the community level have documented cases where AI systems designed for the Global South have failed:

• Excluded eligible beneficiaries due to flawed or biased training data.
• Reinforced existing gender, ethnic, or socioeconomic biases, perpetuating inequality.
• Reduced transparency and contestability of decisions, creating "black box" outcomes.
• Prioritized technical indicators over lived realities and community-defined needs.

In social assistance programs, algorithmic eligibility systems have sometimes removed vulnerable households from benefits without a clear, explainable reason or a functional appeal mechanism. For M&E professionals, this represents not only a technical failure but a profound failure of accountability and ethical practice. The lesson is clear: AI and the Global South must be evaluated not only on what it optimizes, but on who it benefits—and who it harms.

The Digital Divide as a Structural Risk in AI-Enabled Programs

The digital divide remains one of the most persistent challenges in the Global South, and AI can easily deepen it if left unchecked. Many communities face:

When AI systems assume consistent connectivity, digital identity documentation, or standardized data formats, they systematically disadvantage those already at the margins. From an evaluation perspective, this creates a major blind spot. If AI-driven interventions primarily capture data from digitally connected populations, entire groups—often the most vulnerable—may disappear from the evidence base, skewing M&E findings and subsequent decision-making.

Conversely, AI also holds potential to reduce digital inequality when designed intentionally with the Global South in mind. Examples include:

• AI-powered translation and NLP tools supporting under-resourced languages.
• Low-bandwidth data collection and analysis applications.
• AI-assisted monitoring of online gender-based violence.
• Adaptive learning platforms for underserved communities.

The difference in outcome for AI and the Global South lies not in the technology itself, but in whose priorities, contexts, and voices shape its design and deployment from the outset.

Civil Society’s Role in Responsible and Participatory AI

Civil society organizations (CSOs) are essential actors in development ecosystems. They understand local power dynamics, social norms, and community priorities. They also play a critical role in accountability, advocacy, and rights protection. Yet, in AI-related decision-making for the Global South, civil society participation is often informal, limited to implementation phases, or entirely excluded from design and governance discussions.

This exclusion undermines both ethical standards and program effectiveness. A growing body of practice therefore advocates for Participatory AI, an approach that involves affected communities and civil society throughout the AI lifecycle:

For M&E professionals in the EvalCommunity, this aligns closely with established evaluation principles, including utilization-focused evaluation, accountability to affected populations (AAP), human-centered design, and rights-based approaches. Without the active inclusion of civil society, AI and the Global South risks becoming a modern form of data colonialism, where data is extracted from communities without meaningful agency, fair benefit-sharing, or robust accountability for harms.

Four Pathways Toward Inclusive AI in the Global South

1. Building AI Literacy for Civil Society and Evaluators

AI literacy is not about turning evaluators or community activists into data scientists. It is about enabling these critical stakeholders to understand what AI can and cannot do, interpret AI outputs critically, identify bias and exclusion risks, and engage meaningfully in technical decision-making. Simultaneously, AI developers and donor agencies need greater literacy in local contexts, evaluation methodologies, and ethical frameworks like "do no harm."

2. Shifting Decision-Making Power Closer to Communities

Many AI projects fail because they address donor or organizational priorities rather than community-defined problems. Local civil society organizations can help determine whether AI is appropriate or necessary, identify non-technical alternatives that may be more sustainable, and co-design indicators and success metrics that reflect lived experience rather than purely technical benchmarks.

3. Strengthening Civil Society Advocacy Through Evidence

Global AI governance is increasingly shaped by policies, standards, and forums in the Global North. However, Global South civil society often lacks access to these spaces, funding for independent research, and technical support. M&E professionals can play a crucial role by generating credible, locally-grounded evidence on AI impacts, documenting unintended consequences, and translating complex technical issues into clear, policy-relevant insights for advocacy.

4. Prioritizing Data Governance and Community Control

AI systems depend on data, but data governance is often weakest where AI impacts are strongest. Key concerns for AI and the Global South include obtaining meaningful consent in contexts of power imbalance (e.g., humanitarian aid), practicing data minimization, and managing the long-term risks of biometric and digital identity systems. Evaluators must critically examine whether an AI project's data practices align with ethical standards and community control over how their information is used.

Evaluating AI as a Socio-Technical System

One of the most common and dangerous mistakes in AI adoption is treating it as a neutral, objective tool. In reality, AI systems are socio-technical constructs that reflect the values of their designers, the historical and social biases present in training data, and the institutional incentives shaping their use.

Civil society organizations are indispensable partners in answering these questions because they can surface impacts—such as social stigmatization or the erosion of trust—that are invisible in aggregated datasets. Their grounded perspective is vital for a holistic evaluation of AI and the Global South.

Frequently Asked Questions (FAQ)

Key Resources for Practitioners

Conclusion: A Narrow Window for Inclusive AI

AI adoption in international development is accelerating faster than the governance frameworks and participatory mechanisms needed to guide it ethically. This creates a narrow but critical window of opportunity for those committed to equitable development.

If civil society and evaluation professionals are meaningfully involved now, AI can support more equitable service delivery, better-informed decision-making, and stronger accountability to affected populations in the Global South. If not, AI risks reinforcing existing inequalities under the seductive guise of innovation and efficiency, creating a new digital caste system.