The Importance of Decoloniality in Leveraging AI to Achieve the UN Sustainable Development Goals

Decoloniality functions as both a theoretical and practical framework aimed at dismantling the deeply rooted structures, systems of knowledge, and power dynamics that were established during and after colonial times.

Its objective is to address and rise above the enduring legacies of colonialism, specifically concerning the prevailing worldviews, methods of knowledge creation, and governance frameworks found in the Global North (Europe and North America).

In contrast to decolonisation—which typically refers to the formal process of gaining political independence from colonisers—decoloniality explores the deeper, ongoing social, cultural, and epistemological effects of colonialism that persist even after official independence.

These effects include disparities in knowledge systems, global power structures, and entrenched narratives that continue to marginalise the Global South (African, Asian, and other non-Western) knowledge systems.

It is important to emphasize that the conversation around decoloniality should steer clear of the term “indigenous knowledge systems”.

Using this term suggests that knowledge systems from the Global North possess universal legitimacy.

All knowledge systems are indigenous to their context.

Therefore, referring to “indigenous knowledge systems” can often become vague or even patronizing.

This text prefers the broader term “Global South knowledge systems”.

The Essence of Decoloniality

At its core, decoloniality advocates for the prioritization of local, Global South knowledge systems while questioning the so-called “universal” claims of Western epistemology.

It challenges the belief that Western knowledge forms (like science, philosophy, and economics) are inherently superior or objective, promoting the idea of pluriversality—the existence and coexistence of multiple knowledge frameworks.

For instance, Global South viewpoints on nature and sustainability offer invaluable insights for addressing urgent issues such as climate change, aligning with SDG 13 (Climate Action).

By validating Global South perspectives, decoloniality aims to reclaim the cultural identity and epistemic autonomy often oppressed by colonial rule.

In practice, decoloniality manifests in various sectors, such as education, governance, research, and development.

In education, it calls for the integration of Global South knowledge into curricula, ensuring students learn from a diverse range of perspectives rather than predominantly from those of the Global North.

In governance, it involves reassessing legal and policy frameworks to align with community-oriented approaches to justice, health, and economic development.

In the realm of development, it scrutinizes the rationale behind “modernization” models that impose Western economic growth practices, advocating for culturally relevant, context-specific development strategies instead.

The decolonial approach holds especially important relevance in the Global South, where colonial legacies continue to impact developmental trajectories.

By embracing decoloniality, societies can pave the way for more inclusive, equitable, and sustainable developmental pathways, frequently leveraging local knowledge to meet SDGs in culturally pertinent ways.

AI and Decoloniality

The intersection of AI and decoloniality highlights the need to challenge Global North-centric (Eurocentric and Western) paradigms that have steered AI development and application.

Most AI technologies, algorithms, and datasets originate from institutions in the Global North, often reflecting the values, assumptions, and biases prevalent in Eurocentric and Western cultures.

This dominance reinforces existing colonial power dynamics, positioning the Global South as a passive consumer of AI technologies rather than as an active participant.

Decoloniality in AI calls for a reconfiguration of this power dynamic, ensuring that marginalized communities, particularly from the Global South, play integral roles in the creation, ownership, and governance of AI systems.

It underscores the necessity of democratizing AI development by incorporating diverse perspectives, local knowledge systems, and culturally relevant views.

For example, African AI researchers promote African-centric AI initiatives that address local developmental challenges, such as improving healthcare accessibility and fostering sustainable agriculture in more contextually relevant ways than imported AI solutions.

Decoloniality in AI is crucial for tackling algorithmic bias and ensuring that AI systems equitably affect marginalized communities.

Since AI models are often trained on datasets from the Global North, they may fail to recognize or misclassify images, languages, or behaviors found in non-Western contexts.

For instance, facial recognition technologies have shown heightened error rates for individuals of African and Asian descent, raising concerns about racial bias and the misuse of such technologies by authoritarian regimes.

From a decolonial perspective, these issues are not merely technical hiccups but exemplars of epistemic injustice, wherein certain groups are marginalized from knowledge creation.

Decolonizing AI necessitates the construction of inclusive datasets and the engagement of local communities in AI development processes.

By involving Global South knowledge bearers, community organizations, and local research institutions, AI systems can become more equitable, transparent, and inclusive.

Decolonial AI can also advance ethical principles that prioritize human rights, social equity, and SDG 10 (Reduced Inequalities).

Decoloniality and AI for SDG Achievement

Together, decoloniality and AI offer the potential to create pluriversal AI systems—those that recognize the coexistence of diverse knowledge systems and worldviews in their design, development, and application.

This approach rejects the “one-size-fits-all” mentality typical of Western AI models, allowing for localized, context-sensitive AI solutions.

For example, AI tools for sustainable agriculture in Africa could be tailored to incorporate local Global South knowledge regarding soil, climate, and crop cycles, as opposed to solely relying on Western agronomic models.

Such tools would aid in achieving SDG 2 (Zero Hunger) and SDG 13 (Climate Action) in more ecologically and culturally appropriate ways.

Additionally, decolonial AI protects community sovereignty over their data, advocating for data justice frameworks that empower local entities to control the use and purpose of their data.

This focus is essential for countering “data colonialism,” where large tech companies exploit data from the Global South for profit, often without proper consent or benefit-sharing.

By fostering local AI innovation hubs in the Global South, decolonial AI promotes sustainable development while ensuring that communities are co-creators rather than passive consumers of AI technologies.

Ultimately, decoloniality has the potential to significantly enhance AI’s role as a catalyst for inclusive, equitable, and context-sensitive development, propelling progress towards the SDGs while championing equity, cultural justice, sustainability, and human rights.

Indeed, the diverse languages, cultures, values, and insights from Africa must serve as pivotal catalysts in the AI revolution to elevate the quality of life for the continent’s inhabitants.

*This is an excerpt from the book ‘Deploying Artificial Intelligence to Achieve the UN Sustainable Development Goals: Enablers, Drivers and Strategic Framework’