Without indigenous inclusion, AI governance faces legitimacy crisis

New research published in AI & Society suggests that current artificial intelligence (AI) frameworks remain heavily skewed toward Western perspectives, raising concerns about exclusion, cultural erasure, and uneven global representation.

The study "Pluralistic AI governance: Indigenous knowledge and the right of Indigenous peoples to self-determination" analyzes how current AI governance models fail to adequately incorporate Indigenous knowledge systems and principles of self-determination. The research highlights structural imbalances in global AI policy discourse and calls for more inclusive, pluralistic approaches to technology governance.

Western-centric AI governance dominates global policy landscape

The study identifies a clear concentration of influence in the development of AI governance frameworks, with Western institutions, academic bodies, and policy organizations playing a dominant role. This concentration has led to the widespread adoption of governance models that prioritize technical efficiency, risk management, and regulatory compliance, often at the expense of cultural diversity and contextual relevance.

According to the research, most existing frameworks are built around universalist assumptions that may not translate effectively across different societies. These models tend to treat ethical principles as globally applicable without accounting for the social, cultural, and historical contexts in which AI systems are deployed.

This approach creates a disconnect between global governance standards and local realities, particularly in regions with strong Indigenous traditions. The study argues that such frameworks risk imposing external values and priorities, potentially undermining local autonomy and knowledge systems.

The dominance of Western perspectives also shapes how key issues such as data ownership, consent, and accountability are defined. In many cases, these definitions do not align with Indigenous concepts of collective ownership, relational accountability, and community-based decision-making.

Indigenous knowledge systems offer alternative governance models

Indigenous knowledge systems provide valuable insights for rethinking AI governance. These systems emphasize interconnectedness, sustainability, and long-term stewardship, offering a contrast to the short-term, efficiency-driven approaches often seen in current frameworks.

The research notes that Indigenous perspectives view data not merely as a resource but as part of a broader relational ecosystem involving people, land, and community. This perspective challenges dominant models that treat data as an extractive asset, raising important questions about consent, ownership, and ethical use.

Incorporating Indigenous knowledge into AI governance could lead to more holistic and context-sensitive approaches. For example, principles such as collective benefit, respect for community autonomy, and intergenerational responsibility could inform the design and deployment of AI systems.

The study also underscores the importance of Indigenous participation in decision-making processes. Without meaningful involvement, efforts to integrate Indigenous perspectives risk becoming symbolic rather than substantive. Genuine inclusion requires structural changes that allow Indigenous communities to shape governance frameworks on their own terms.

Data sovereignty and self-determination at the core of future AI policy

The concept of data sovereignty emerges as a key theme in the study, highlighting the need for communities to have control over how their data is collected, used, and shared. For Indigenous groups, this is closely tied to broader struggles for self-determination and cultural preservation.

The research argues that current AI systems often operate within data ecosystems that prioritize accessibility and scalability over local control. This can lead to situations where data from Indigenous communities is used without adequate consent or benefit-sharing, reinforcing patterns of extraction and inequality.

To address these challenges, the study calls for governance models that embed data sovereignty as a core principle. This includes mechanisms for community consent, transparent data practices, and equitable distribution of benefits derived from AI systems.

The study also highlights the need for legal and institutional frameworks that recognize Indigenous rights in the digital domain. As AI technologies continue to evolve, ensuring that these rights are protected will be critical for preventing new forms of marginalization.