AI and Indigenous Peoples at the Nexus of the SDGs

Article by: Carlotta de Carolis Villars, Open Dialogues International Foundation

Pandey, J. (2023) | Medium

In its 24^th session, the UN Permanent Forum on Indigenous Issues proclaimed the necessity for Indigenous Peoples to actively participate in shaping the future of AI throughout its lifecycle, from training to governance. Both AI and the rights of indigenous communities are individually at the heart of multiple SDGs and are being incorporated in the UN Agenda, but as the technology evolves the two seem to be increasingly incompatible. 

The rise of AI and its integration in an exponentially increasing number of tasks, professions and mechanisms seems to be unstoppable, and fits right into SDG 9 (Industry, Innovation and Infrastructure). However, the current explosion of AI technologies and the water, energy and land-intensive data centres that support them are currently at the antipodes of most other SDGs, particularly environmental goals (SDGs 13, 14 and 15). Less evidently, poorly distributed advancements in AI technology and profits from AI are undermining SDG 10, Reduced Inequalities. Indigenous communities are among the most vulnerable groups in the Fourth Industrial Revolution: they are facing territory and resource takeover, environmental repercussions, increase in income and education inequality, and even the return of what scholars have flagged as colonial dynamics. 

This article firstly outlines the challenges specific to indigenous communities that AI technology represents throughout its life cycle, from the construction of specialised digital infrastructure to its legacy of income inequality and harmful cultural bias. Subsequently, it highlights the ways in which indigenous people are instrumentalising this tool to protect their identity and livelihoods. And finally, it summarises the ways in which indigenous cultures, knowledge systems and actorness must be embedded in AI models for them to not only minimise harm but empower vulnerable groups. 

By taking these steps, the article aims to provide a clear account of the interactive pathways of AI and indigenous communities at the level of the UN SDGs. 

Territory takeover

The first and most evident impact of AI expansion on indigenous community is the territory takeover that the latter have experienced in the past couple of years, especially in the United States. The rapid expansion threatens to not only chip on the already diminished indigenous occupied and owned lands left, but to eat away territories and water resources that are sacred to local cultures and key to local legends and rituals. Fragile traditional knowledge systems and treaty rights are also deeply connected to these areas, including some that are part of the UNESCO Intangible Cultural Heritage. 

In addition to the threats associated with this expansion, the methods used to come to the sale of land for large-scale digital projects often sits between unfairness and unlawfulness: in Canada, the Sturgeon Lake Cree Nation opposed the proposal of the world’s largest planned industrial park after no consultation occurred between the Municipality and the First Nation, a legal requirement due to the overlap of the plan with their territory. The lawsuit was turned down and the sale continued. 

Environmental repercussions

AI data centres are among the most energy-intensive technologies of this day and age, and because their expansion has yet to stimulate an equally fast growth in the green transition, this also makes them among the most polluting. Data centres also require incredibly high quantities of water for cooling, which draws from essential freshwater sources in the local area. Because of this, indigenous communities are meeting increasing challenges around water collection and management, as well as in the agricultural and ecological sectors.

While this strain on environmental resources is harmful for all human centres, it takes a higher toll on communities that are closely reliant on their local ecosystems and their natural functioning for resource extraction. 

Increase in inequality: income and digital divide

AI is developing new strands of work in a myriad of sectors, providing creative opportunities to innovators and pioneers worldwide. However, only those who have access to these technologies can even consider being part of this professional elite, and because of geographical and discriminatory isolation, indigenous people are lagging. The World Summit of the Information Society has also flagged the structural obstacles faced by indigenous people in accessing new technologies on top of tech careers, and this group has been proven to suffer from both barriers of entry and underrepresentation in the highest-paying spheres of tech-related jobs. Moreover, the creative destruction paradox is truer in AI than ever, with a good quantity of jobs becoming automated in the next two decades. A WEF report unveils that AI-led automation has an ethnic bias, discriminating POCs and especially indigenous workers. 

Reinforcement of colonial dynamics

In addition to the more obvious subtraction of land and natural resources, there are two paradoxically opposing ways in which the way AI is being developed perpetrates colonial dynamics: one occurs through the exclusion of indigenous voices, and the other through the theft of indigenous heritage and practices without the consent and guidance of appropriate representatives. 

The former involves excluding indigenous representatives from AI model trainings, leading to a homogenisation of practices into Western frameworks “that do not necessarily reflect or serve Indigenous community needs”. The latter, identified as ‘data colonialism’ or ‘data extractivism’, involves the usage of information extracted from Indigenous Knowledge Systems without a proper overview or an adapted system ensuring the accuracy of the content produced; so, while indigenous inputs are not considered, their intellectual property is still instrumentalised. Because of GenAI’s prioritisation of form over content or ethics, it often recycles material from indigenous communities to regurgitate false or inaccurate content that spreads misinformation about those same communities.

Harmful cultural bias

Because of the prevalence of data based on Western frameworks and epistemologies in AI training models which excludes or misrepresents indigenous knowledge, AI can reproduce and reinforce harmful cultural biases and exclusion. This is particularly dangerous in contexts where previous knowledge of non-indigenous people towards indigenous communities is scarce: indigenous communities are often esoteric and obscure because of forced isolation or strong community practices that tend towards the inwards securitization of knowledge, so replacing information that comes directly from indigenous people with AI-generated content is particularly detrimental and can lead to further ostracisation and othering. 

AI opportunities for indigenous inheritance

Despite these threats, could AI itself play an active role towards the protection of vulnerable communities? 

AI technologies do represent an incredible advancement for all their users across an immense number of fields, and this value can be and has been honed by indigenous communities as well, including to strengthen their very indigenous identity by preserving intergenerational knowledge, increasingly unused language systems, and cultural practices. 

Probably the most evident case of this use of AI comes from computer scientist Michael Running Wolf, at the Mila-Quebec Artificial Intelligence Institute, who is developing the First Languages AI Reality, a tool “designed to respect data sovereignty and linguistic self-determination” that includes speech recognition models for over 200 endangered languages. 

These projects already promise great advancements in the maintenance of indigenous languages and culture at the risk of extinction, but as of now their scale is still quite modest. Moreover, a very small proportion of computer data scientists (both at the bachelor and doctorate levels) are indigenous . Some initiatives are also tackling this challenge: IndiGenius, Rewriting the Code and Wihanble S’a Center for Indigenous AI are training indigenous computer science students in the preservation of heritage through this technology. 

Moreover, the ethical incorporation of indigenous heritage into AI training models would be an added value well beyond indigenous communities, as these technologies that a staggering number of people are blindly using as reliable sources could do with the principles of reciprocity, sustainability, and collective well-being brought by Indigenous Knowledge Systems.

Steps forward in light of the reconciliation of the SDGs

AI represents an incredible opportunity for indigenous individuals and communities worldwide through digital integration, job creation and most importantly the preservation of identity and cultural heritage. However, this must happen following crucial safeguards: 

1. Data centre expansion and construction into indigenous territories and drawing from energy sources used by indigenous communities must be based on and respect legal agreements with their representatives. 

2. Data centres should be built following technical guidelines on safe energy and water consumption, especially from local sources. 

3. Indigenous Knowledge Systems, epistemologies and academic information on indigenous peoples must be incorporated in AI training models to avoid cultural biases or the exclusion of indigenous narratives, but this must occur with respect of cultural secrecy practices and data privacy, and with the oversight of indigenous communities to ensure the accuracy of reported material and avoid intellectual/cultural theft. 

4. Resources must be dedicated to scholarships and educational programmes towards native students in STEM, with a focus on computer science. 

Without these guidelines, the uncontrolled expansion of AI risks disrupting already fragile livelihoods by depleting local and regional ecosystems, reinforcing biases and community-wide isolation patterns, and replicating colonial dynamics of land, resource and knowledge appropriation, all while excluding the voices of the oppressed, ultimately undermining the principles of the 2030 UN Agenda. 

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