Decoloniality is a theoretical and practical framework aimed at dismantling the structures, knowledge systems, and power dynamics that were established during and after colonial rule.
It seeks to challenge and move beyond the enduring legacy of colonialism, particularly the dominance of Global North (European and North American) worldviews, knowledge production, and governance systems.
Unlike decolonisation, which often refers to the formal process of gaining political independence from colonising powers, decoloniality focuses on the deeper and more profound, ongoing social, cultural, and epistemic impacts of colonialism that persist even after formal independence.
These effects include inequalities in knowledge systems, global hierarchies of power, and entrenched narratives that continue to marginalise Global South (African, Asian, and other non-Western) knowledge systems.
It is instructive to emphasise that the decoloniality discourse must reject the term “indigenous knowledge systems”.
Using it suggests that Global North knowledge systems are universal.
Every knowledge system is indigenous somewhere.
Hence, the phrase “indigenous knowledge system” becomes nebulous if not downright condescending and patronising.
This treatise uses the broader term “Global South knowledge systems”.
The Essence of Decoloniality
At its core, decoloniality advocates for the recentring of local, Global South knowledge systems while questioning the “universal” status of Western epistemology.
It challenges the idea that Western modes of knowledge (science, philosophy, and economics) are superior or objective and instead
emphasises the need for pluriversality—the coexistence of multiple knowledge systems.
For example, Global South perspectives on nature and sustainability offer valuable insights into addressing contemporary challenges such as climate change and align with the goals of SDG 13 (Climate Action).
By restoring the legitimacy of Global South worldviews, decoloniality seeks to reclaim cultural identity and epistemic sovereignty that were often suppressed under colonial rule.
In practice, decoloniality manifests across education, governance, research, and development.
In education, it calls for the inclusion of Global South knowledge in curricula, ensuring that students learn from diverse perspectives rather than solely from Global North ones.
In governance, it involves rethinking legal and policy frameworks to reflect local, community-driven approaches to justice, health, and economic development.
In development, it questions the logic of “modernisation” models that impose Western-style economic growth, instead advocating for context-specific, culturally relevant development models.
The decolonial approach is particularly relevant in the Global South, where colonial legacies continue to shape development trajectories.
By embracing decoloniality, societies can create more inclusive, just, and sustainable development pathways, often leveraging local knowledge to achieve the SDGs in ways that are culturally and contextually appropriate.
AI and Decoloniality
The intersection of AI and decoloniality highlights the need to challenge Global North-dominated (Eurocentric and Western) frameworks that have shaped AI development and deployment.
Most AI technologies, algorithms, and datasets are designed and controlled by institutions in the Global North, often reflecting the values, assumptions, and biases of Eurocentric and Western societies.
This dominance reinforces existing colonial power structures, in which the Global South becomes a passive consumer of AI technologies rather than an active co-creator.
Decoloniality in AI calls for a shift in this power dynamic, ensuring that marginalised communities, particularly those from the Global South, play a central role in the development, ownership, and governance of AI systems.
It emphasises the need to democratise AI development by including diverse voices, local knowledge systems, and culturally relevant perspectives.
For example, African AI researchers have called for African-centred AI that addresses local development challenges, such as improving healthcare access and sustainable farming, in ways that are more contextually relevant than imported AI models.
Decoloniality in AI is crucial to addressing algorithmic bias and the unequal impact of AI systems on marginalised communities.
Since AI models are often trained on datasets collected in the Global North, they may fail to recognise or misclassify images, language, or behaviours from non-Western contexts.
For example, facial recognition software has been shown to exhibit higher error rates for people of African and Asian descent, raising concerns about racial bias and its potential misuse by authoritarian regimes.
From a decolonial perspective, these issues are not merely technical flaws but reflections of epistemic injustice, in which certain groups are excluded from knowledge production.
Decolonising AI requires developing inclusive datasets and engaging local communities in AI design processes.
By involving Global South knowledge holders, community organisations, and regional research hubs, AI systems can be made more equitable, transparent, and inclusive.
Decolonial AI can also promote ethical AI principles that prioritise human rights, social justice, and SDG 10 (Reduced Inequalities)
Decoloniality and AI-Based SDG Achievement
Decoloniality and AI together offer the possibility of creating pluriversal AI systems—systems that recognise the coexistence of multiple knowledge systems and worldviews in AI design, development, and application.
This approach moves away from the “one-size-fits-all” logic of Western AI models, instead allowing for localised, context-aware AI solutions.
For example, AI-driven tools for sustainable agriculture in Africa could be designed to incorporate local Global South knowledge about soil, climate, and crop cycles rather than relying solely on Western agronomic models.
Such tools support SDG 2 (Zero Hunger) and SDG 13 (Climate Action) in more ecologically and culturally relevant ways.
Additionally, decolonial AI prioritises the sovereignty of communities over their data, advocating data justice frameworks that ensure local communities control how their data is used and for what purpose.
This is essential in addressing the problem of “data colonialism”, where big tech companies extract data from the Global South for profit without proper consent or benefit-sharing.
By fostering local AI innovation hubs in the Global South, decolonial AI promotes AI for sustainable development while ensuring that communities become co-creators, not just consumers, of AI technologies.
Indeed, decoloniality can play a critical role in deploying AI to achieve the SDGs by ensuring that AI development, governance, and application are inclusive, equitable, and contextually relevant.
As explained earlier, traditional AI models are often rooted in Western epistemologies, which fail to recognise or integrate the local knowledge systems, values, and lived realities of communities in the Global South.
Decoloniality challenges this imbalance by promoting the development of pluriversal AI systems that respect diverse worldviews and centre the voices of historically marginalised communities.
This is especially important in achieving SDG 10 (Reduced Inequalities), as decolonial AI prioritises local ownership, data sovereignty, and the co-creation of solutions that are tailored to local development needs.
For example, in agriculture (SDG 2—Zero Hunger), AI-driven tools designed using decolonial principles would incorporate local Global South farming knowledge, local weather patterns, and ecological wisdom to create context-specific precision farming solutions.
In health (SDG 3—Good Health and Well-Being), decolonial AI could support the development of culturally relevant health diagnostics that consider health’s social and cultural determinants.
By promoting data justice, decoloniality ensures that communities retain control over their data, preventing data colonialism by large tech corporations.
It is important to emphasise that decoloniality ensures AI systems are inclusive, culturally relevant, and community-driven, enabling local ownership and knowledge sovereignty, thereby supporting SDG 10 (Reduced Inequalities) and SDG 16 (Peace, Justice, and Strong Institutions).
Ultimately, decoloniality can strengthen AI’s role as a driver of inclusive, just, and context-sensitive development, accelerating progress towards the SDGs while promoting equity, cultural justice, sustainability, and human rights.
Indeed, the diverse African languages, cultures, values, and insights must be key drivers of the AI revolution if the Intelligent Age is going to occasion improvements in the quality of life of 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‘
