Decolonizing data ensures AI serves all communities equitably by addressing biases, promoting data sovereignty, and fostering inclusive, ethical practices.

Artificial intelligence is no longer a futuristic abstraction; it has woven itself into the very fabric of our lives. From shaping online recommendations to deciding who gets a loan, AI influences critical decisions, often before we even realize its involvement. Its promises are tantalizing: faster processes, unprecedented efficiency, and wider access to information.

Yet, as AI takes on a larger role in shaping our society, a pressing question emerges: Will its benefits be universal, or will they favor the powerful at the expense of the marginalized?

AI is not neutral. For all its sophistication, AI mirrors the world as it exists—complete with its biases and inequities—rather than the world we aspire to create. The data fueling these systems reflects historical and societal prejudices. When biased data trains AI, it scales those prejudices, transforming historically marginalized groups into algorithmically marginalized ones. Instead of dismantling inequalities, AI often entrenches them.

Bias at Scale: A Troubling Legacy

The consequences of algorithmic bias are both well-documented and deeply troubling. Consider the COMPAS risk assessment tool, which ProPublica exposed in 2016. It disproportionately flagged Black defendants as high risk for recidivism, even when their records were similar to their white counterparts. Another glaring example comes from healthcare: AI diagnostic systems often yield less accurate results for Black patients than for white patients, further exacerbating disparities in medical care.

Such biases are not anomalies; they directly result from how data is collected and used. This issue is encapsulated by the concept of “data colonialism,” where data extracted from communities benefits others while providing little to those it was derived from. As Nithya Ramanathan and her colleagues note, data colonialism parallels historical exploitation practices, with communities reduced to mere data sources rather than active stakeholders.

Reimagining Data: From Exploitation to Sovereignty

Historically, data collection practices have been rooted in systems of control, categorizing and simplifying complex cultures to serve dominant interests. This “data extraction” perpetuates the inequities of the colonial era in a digital form. In a world where data equals power, marginalized communities are demanding sovereignty over their information. “Data sovereignty” shifts control from external entities to the communities themselves, empowering them to decide how their data is collected, stored, and utilized.

“Decolonizing data” seeks to disrupt exploitative frameworks, emphasizing community-led stewardship. By ensuring that data serves those it represents, this approach reclaims agency for historically marginalized groups. It’s not just about technical precision but about cultural awareness—recognizing that data isn’t mere numbers but a reflection of people’s lived realities.

The Case for Inclusive AI

Building equitable AI starts with diversifying its foundations. Historically excluded groups must be intentionally represented in data sets, particularly in languages, dialects, and regions overlooked by major AI projects. As Phaedra Boinodiris argues in “The importance of diversity in AI isn’t opinion, it’s math,” diverse data leads to better predictions and fairer outcomes. When AI systems integrate varied perspectives, they are better equipped to serve everyone.

This principle aligns with the “Diversity Prediction Theorem,” which underscores the mathematical advantages of inclusivity. Yet inclusion isn’t just a statistical imperative—it’s a moral one. By expanding representation in data, AI can move closer to fulfilling its promise of democratizing opportunity.

Governance and Accountability: A Shared Responsibility

At the heart of decolonizing data lies the concept of ethical governance. Indigenous-led frameworks offer a model for responsible AI, prioritizing informed consent, community consultation, and respect for traditional knowledge. As Maggie Walter and Tahu Kukutai emphasize, data sovereignty must position communities as primary authorities over their data, safeguarding their interests against exploitation.

Partnerships between native organizations and AI developers can co-create systems that are not only technically robust but culturally sensitive. Governments, too, have a role to play by recognizing data-producing communities as beneficiaries of the value their data generates. Effective governance isn’t about stifling innovation; it’s about making smarter, more inclusive choices that respect human dignity.

Toward a More Inclusive Future

Efforts to decolonize data are already gaining traction. Initiatives like SAMINOR in Norway exemplify community-driven approaches to ethical data collection, while movements such as Maiam nayri Wingara in Australia and Te Mana Raraunga in New Zealand champion Indigenous data sovereignty. These initiatives are laying the groundwork for a more equitable data landscape.

Tech companies are also beginning to invest in models for underrepresented languages and explore ethical standards for data use. While these efforts are promising, they remain nascent. After years of neglecting “low-resource” languages, the industry has a long way to go in ensuring that AI reflects the full spectrum of human diversity.

Decolonizing data is not a mere academic exercise but a moral and practical imperative. By embracing a community-centered approach, we can ensure that AI evolves to serve everyone—not just the powerful. In doing so, we can harness AI’s transformative potential to build a truly inclusive, equitable, and just future.