Decoloniality framework aims to make AI development fairer and more inclusive

The study argues that by failing to consider power imbalances, mainstream frameworks inadvertently reinforce the very inequities they are meant to mitigate. It points to the absence of local knowledge systems, such as indigenous or community-based epistemologies, which could help create more equitable and context-sensitive AI solutions.

A new study calls for a fundamental shift in how AI technologies are evaluated for their social and ethical impacts. The research, published in AI & Society and titled “Decoloniality impact assessment for AI”, argues that existing impact assessment frameworks often ignore the historical and structural inequalities embedded in AI’s global development and deployment.

The study highlights that current assessments, though strong on human rights, privacy, and safety, rarely address how AI reproduces colonial patterns of power through data extraction, labor exploitation, and resource use, especially in the Global South.

Gaps in current AI impact assessments

The authors conducted a narrative review of literature, including 39 key documents from academic and policy sources. They found that mainstream AI impact assessments focus on technical, legal, and ethical compliance but often assume Global North standards as universal benchmarks.

This approach, they warn, risks perpetuating “data colonialism” - the large-scale extraction of data from marginalized communities without equitable benefits. It also overlooks the environmental and labor costs of AI development, such as the exploitation of workers in data annotation and the intensive extraction of minerals for hardware manufacturing.

The study argues that by failing to consider power imbalances, mainstream frameworks inadvertently reinforce the very inequities they are meant to mitigate. It points to the absence of local knowledge systems, such as indigenous or community-based epistemologies, which could help create more equitable and context-sensitive AI solutions.

Introducing the decoloniality impact assessment

To address these gaps, the researchers propose the Decoloniality Impact Assessment (DIA) - a framework designed to integrate questions of power, equity, and local agency into every phase of the AI lifecycle. DIA aims to complement existing assessment models rather than replace them, offering practical tools to surface coloniality risks and guide mitigation strategies.

The DIA approach covers the full lifecycle of AI projects, including ideation, design, development, deployment, commercialization, and governance. By intervening early in the process, DIA seeks to prevent extractive practices before they become entrenched.

The framework suggests concrete tools and indicators such as:

• Positionality and power mapping during ideation to identify who defines problems and who stands to benefit.
• Inclusivity indices and design reflexivity logs to ensure that local knowledge and needs inform system design.
• Testing and validation logs with a focus on performance equity across diverse contexts.
• Community consent registers and benefit-sharing indices for deployment to ensure affected populations retain agency.
• Market equity risk scores and community oversight charters to guide commercialization and long-term governance.

A traffic-light evaluation system, green, amber, red, enables teams to monitor progress and highlight areas needing urgent attention.

Aligning DIA with existing frameworks

DIA, as the authors state, is not meant to replace well-established standards and tools. Instead, it can be integrated into frameworks such as the European Union’s Fundamental Rights Impact Assessment, the Council of Europe’s HUDERIA tool, and ISO/IEC standards for AI risk management.

This integration allows developers and policymakers to retain familiar compliance processes while adding a critical lens to address issues of power, inclusion, and equitable value distribution. By doing so, they can better align AI innovation with principles of justice and sustainability.

The study stresses that effective adoption of DIA depends on the collaboration of diverse stakeholders, AI developers, funders, social scientists, affected communities, and policymakers. It also calls for capacity-building initiatives and funding to ensure that resource-limited organizations and communities can meaningfully participate in the process.

A path forward for inclusive and equitable AI

The researchers argue that a decolonial perspective on AI is essential to prevent the technology from reinforcing historical inequities. As AI systems increasingly shape decision-making in areas such as healthcare, education, and public administration, overlooking power dynamics can deepen social divides and marginalize vulnerable populations.

By embedding DIA into existing assessment regimes, the authors envision a more just and participatory approach to AI development, one that recognizes diverse knowledge systems, respects collective consent, and ensures fair distribution of benefits.

The study highlights that this approach is particularly relevant in regions where AI is deployed but not locally developed, making communities dependent on external technologies and governance standards. It underscores that accountability must extend beyond technical metrics to include relational and structural considerations.