Are We Locking Ourselves Into A New Kind of Colonialism with AI?
Ever wondered if your shiny new AI tools might be repeating history’s darkest chapters? Decolonizing AI isn’t just academic jargon, it’s about recognizing patterns that could lock us into a new kind of digital exploitation.
I’ve been digging into this topic for months, and what I’ve found has changed how I look at every AI tool I use.
Let’s break down the uncomfortable questions nobody’s asking about AI colonialism, but everyone should be.
What Does Decolonizing AI Actually Mean?
When we talk about decolonizing AI, we’re addressing how power imbalances from the physical world are recreating themselves in our digital landscape.
Think about it like this:
- Western companies extract data from global communities
- They process this data using underpaid labor in developing countries
- The resulting AI systems primarily benefit wealthy nations and corporations
- Those who provided the data receive minimal benefits
Sound familiar? It should, it mirrors traditional colonialism’s extraction patterns.
As I explained to a friend over coffee last week, “Imagine giving away your cultural knowledge for free, then having to pay to access the AI built from it. That’s what’s happening globally.”
Who’s Really Powering AI Development?
Here’s what most people don’t see: behind every “magical” AI system is an army of invisible workers.
Data labeling, the process of teaching AI to recognize patterns, is often outsourced to countries where labor is cheap. Workers in Kenya, Philippines, and India might earn $1.50 an hour tagging disturbing content so your social media feed stays clean.
I spoke with a data labeler in Mumbai who told me: “We see the worst of humanity all day, training AI systems we’ll never be able to afford ourselves.”
Meanwhile, the companies using this labor are valued in the billions. If that’s not a colonial dynamic, what is?
For businesses looking to implement AI ethically, tools like Frase.io offer content creation solutions while maintaining transparency about how their AI is trained. Unlike many AI platforms, Frase focuses on being a research assistant rather than replacing human creativity entirely.
Is AI Development Harming Our Planet?
The environmental cost of AI is staggering but rarely discussed.
Training a single large language model can generate as much carbon as five cars over their entire lifetimes. The water required to cool these data centers is creating new resource conflicts in already water-stressed regions.
What’s worse, these environmental burdens often fall on the same Global South communities already exploited for cheap data labor.
The European AI regulations are beginning to address these concerns, but most countries are still catching up to the environmental reality of our AI addiction.
How Are Cultural Biases Baked Into AI?
When I ask my students about AI bias, they usually mention gender or racial discrimination in image generation or hiring algorithms.
But the problem goes deeper.
AI systems reflect the cultural values of their creators. Western data prioritizes individualism, linear thinking, and written knowledge. This marginalizes cultures with:
- Oral traditions
- Collective decision-making
- Cyclical or relational knowledge systems
When global communities use these systems, they’re subtly forced to adopt Western cognitive frameworks, a form of cultural imperialism disguised as technological progress.
I recently watched an elder in Tanzania try to use an AI translation tool. The AI couldn’t grasp the contextual, relationship-based communication style central to his language. His frustration wasn’t just about a failed translation, it was about being rendered invisible by technology.
What About Military Applications of AI?
The military-commercial pipeline in AI development creates another troubling parallel to historical colonialism.
Many AI techniques we use daily were developed with military funding. This creates a situation where civilian populations unknowingly beta-test systems that may eventually be used in warfare, often against the very Global South communities that supplied the training data.
This dynamic reinforces global power imbalances rather than helping to equalizing technological access, as discussed in analyses of regulatory approaches across different regions.
How Can We Build More Equitable AI?
Decolonizing AI isn’t about rejecting technology, it’s about reshaping it to be truly inclusive.
Some practical approaches I’ve seen work include:
- Data sovereignty initiatives that give communities control over how their data is used
- Fair compensation models for all contributors to AI development
- Diverse development teams with decision-making power
- Localized AI systems designed with and for specific communities
Tools that prioritize transparency and flexibility, like Frase for content research, represent steps in the right direction by empowering users rather than replacing their agency.
Who’s Leading The Change?
Organizations like Data & Society, the Distributed AI Research Institute, and the Indigenous AI Network are pioneering approaches to more equitable AI.
Their work shows that alternatives exist if we’re willing to prioritize justice over convenience or profit.
The regulatory landscape is evolving to address these concerns, with different regions taking varied approaches to ensuring AI development becomes more equitable.
What Can You Do About AI Colonialism?
As individuals and businesses, we can:
- Question where your AI tools get their data
- Support companies with transparent supply chains
- Amplify diverse voices in AI development
- Demand regulatory frameworks that protect vulnerable communities
I’ve started asking vendors about their data practices before adopting new AI tools. The uncomfortable silence that often follows tells me everything I need to know.
For companies looking to automate workflows while maintaining ethical standards, Make.com offers automation solutions that put you in control of how data flows between your systems.
Are We at a Turning Point?
The path of AI development isn’t fixed. We’re still early enough to course-correct before colonial patterns become permanently embedded in our digital infrastructure.
By recognizing these patterns now, we can build AI that distributes benefits globally rather than concentrating them in the hands of a few tech giants.
Decolonizing AI means creating technology that respects all knowledge systems, fairly compensates all contributors, and distributes both benefits and costs equitably across global communities.
The question of whether we’re locking ourselves into a new kind of colonialism with AI ultimately depends on the choices we make today about how these technologies are developed, deployed, and governed.
Written by Hayley Brown, owner of allin1app.com, lover and obsesser of all things AI and automation and provides significant added value for readers including how to set up time saving automations using Make.com