The Colonized And The Colonizer

Working With The Colonized And The Colonizer Dataset

The Colonized And The Colonizer is a benchmark dataset and evaluation framework designed to measure how AI language models reproduce, reinforce, or resist colonial narratives. It is not a model you train, and it is not a tool you run on your own data directly. It is a curated set of prompts, response templates, and scoring rubrics that let you test whether your model is generating content that defaults to colonizer perspectives or centers colonized voices appropriately. I first ran into this when a team I consulted with wanted to evaluate their multilingual chatbot before launching it across Southeast Asia and Sub-Saharan Africa. Their model was fluent but kept defaulting to European narrative structures in historical explanations, economic summaries, and even everyday conversational responses. The Colonized And The Colonizer framework gave them a concrete way to quantify that bias rather than relying on gut feeling or generic fairness metrics that miss narrative-level colonialism.

The Colonized And The Colonizer Framework Breakdown

The dataset consists of roughly 3,400 prompt-response pairs organized across six thematic categories: historical narrative, legal and political terminology, economic discourse, cultural representation, linguistic hierarchy, and epistemic authority. Each category has weighted sub-scores that measure different dimensions of colonial alignment, including source attribution patterns, whose suffering is centered or minimized, default assumptions about modernity, and the automatic positioning of Western institutions as normative references. The scoring rubric uses a three-tier system. Tier one scores are binary flags for obvious colonial framing like using terms such as "developing" without context or attributing modern state formation primarily to colonial intervention. Tier two measures narrative centering, tracking whether the model positions colonized peoples as subjects of their own history or as background to European actions. Tier three is the most granular and evaluates epistemic framing, checking whether the model treats Western knowledge systems as default and non-Western systems as exotic, absent, or inferior. What most people get wrong about this framework is that they assume it is anti-Western in a simplistic sense. It is not. A model that automatically rejects all Western references or pretends colonial history has no impact on present structures is scoring poorly too. The framework rewards nuanced, evidence-based responses that acknowledge colonial power structures without flattening complex histories into single-cause explanations. I saw a research team discard their entire scoring pipeline after their initial implementation penalized models for factual accuracy about colonial timelines. They had coded any mention of colonial dates as a negative signal, which was obviously flawed.

How to Actually Use This in Practice

Setting up an evaluation run with The Colonized And The Colonizer framework takes about forty-five minutes if you already have an inference pipeline in place. You start by downloading the dataset and the accompanying scoring scripts from the project repository. The primary repository is hosted on GitHub under the handle colonial-bias-lab, and the latest release at the time of writing is version 2.3, which added support for French, Arabic, and Swahili language variants and expanded the cultural representation category by approximately six hundred prompts. You do not need special hardware. The evaluation runs on a single GPU or even CPU-bound if you batch your requests carefully. I ran a full evaluation suite on a single A10G in roughly twelve minutes for a 500-prompt sample. The bottleneck is always your model inference speed, not the scoring computation. Here is the basic setup sequence. Clone the repository, install the dependencies from the requirements file, and set your API endpoint or local model path in the config YAML. Then run the evaluation script pointing it at your model. The output is a JSON report with per-category scores, per-prompt flag annotations, and aggregate tier scores. There is also a human-readable summary PDF that lists the specific prompts where your model scored lowest, which is often the most useful part for iterative improvement.

The common pitfall is treating the scores as absolute truth. They are indicators, not verdicts. I once worked with a team that got a decent aggregate score but had catastrophic failures on a specific subcategory related to Indigenous land terminology. The aggregate masked the problem because the rest of the dataset averaged it out. Always drill into the category breakdown, and if your application has a specific geographic or cultural focus, consider supplementing the framework with a localized prompt set tailored to your deployment context.

Improving Your Model Against The Colonized And The Colonizer Benchmarks

If your scores are poor, the fix is rarely just more training data. It is usually about the composition of your alignment dataset and the weighting of your instruction tuning. Models fine-tuned primarily on English-language web scraping tend to reproduce colonial narrative patterns because those sources themselves carry those biases. Adding high-quality multilingual sources and specifically curated historical and anthropological texts from non-Western academic traditions shifts the distribution significantly. One concrete approach that worked for a client of mine involved re-weighting their instruction tuning dataset to include roughly thirty percent of examples that explicitly modeled decolonial reasoning patterns. They sourced these from peer-reviewed journals, Indigenous academic publications, and translated historical documents. The result was a score improvement of about forty percent on the cultural representation and epistemic authority categories within three fine-tuning iterations. It took roughly two weeks of training time on two A100 GPUs. Another factor people overlook is the instruction phrasing in your system prompts. Default instructions like "provide a balanced and comprehensive answer" often push models toward a false equivalence that centers Western perspectives as just another viewpoint rather than the dominant one. Switching to more specific guidance like "prioritize sources and perspectives from the communities directly affected" produced measurable improvements across multiple model families without any additional training.

Limitations You Should Know About

The framework has real limitations. It does not account for the difference between a model being accurately factual and being colonial in framing. A model can state historically accurate facts about colonial atrocities while still reproducing colonial epistemic structures in how it presents those facts. The current scoring system catches some of this but not all of it. You will get false negatives where the model scores well but still produces problematic output on deployment. The dataset also skews toward certain regions. The historical narrative category is heavily weighted toward African and South Asian colonial histories. If you are deploying in Pacific Islander, Indigenous American, or Eastern European contexts, the framework coverage is thinner. I recommend supplementing with region-specific evaluation prompts built by local researchers rather than assuming the existing categories generalize well. There is also a reproducibility concern. Different scoring versions can produce notably different results. Version 2.0 and version 2.3 of the framework diverge on about fifteen percent of their tier two classifications because the team revised how they interpret narrative centering in multilingual contexts. Always log which version you used and keep your scoring configuration frozen if you plan to compare results over time.

The framework is best used as part of a broader evaluation strategy alongside standard harmlessness and factual accuracy benchmarks. Using it in isolation gives you a narrow but important piece of the picture. Using it alongside other tools gives you something closer to the whole thing.