Research
The science behind the receipt
One research question drives everything we ship: can you find where a concept — race, religion, toxicity — lives inside an AI model, remove its influence without damaging anything else, and prove both to someone who doesn’t trust you? Below is what we can show, how we tested it, and what we don’t claim.
The program at a glance
Remove one concept. Leave the rest measurably untouched.
The standard tools for removing a concept from a model’s representation — the ones most fairness teams use today — work like a thick eraser: the concept goes, and so does much of the meaning around it. That collateral damage is why bias fixes so often break accuracy somewhere else.
Our method builds a frame: a set of mutually perpendicular directions in the model’s embedding space, one per concept. Because the directions cannot lean on each other, removing one concept’s influence leaves the others measurably intact — in our tests, off-target damage is 4–6.5× lower than the standard removal methods. Every removal produces a receipt showing exactly how little else changed.
The result is a property of the method, not of one lucky model. It replicates across random seeds, across concepts, and across publicly available models we did not train — which is what lets Bhala run as an independent layer on top of the AI you already use.
Reading a concept out of a frozen model.
Before you can remove a concept’s influence, you have to find it. We detect concepts directly in the geometry of a model’s embedding space — no retraining, no access to the model’s training data, no cooperation from the model’s vendor required.
Because detection reads structure rather than surface keywords, it holds up where keyword filters fail: coded language, typos, leetspeak, sarcasm, and counter-speech that quotes the hate it condemns. The same approach powers our production moderation API.
The decisive measurement
“I hate X” (P=0.87) and “Saying I hate X is bigoted” (P=0.10) share 80% of their surface tokens but receive 9× different hate scores. Reading the use/mention distinction from structure is what keyword-based moderation cannot do.
Steerable meaning — proven across three languages
Bhala is the first model that lets you steer a sentence's meaning at inference time. Tell it to remove a specific bias, flip sentiment, or redirect an intent — and an independent classifier (the kind your team would deploy in production) confirms the change took effect. Reduce bias by 77–100% on the categories you care about, without retraining.
Other sentence models — including the ones from the largest US labs — are optimized for similarity search, not for being steered. You can compare two sentences in their space, but you cannot reliably change one. Bhala is built so that named directions in meaning behave like real controls. No other production-ready model offers this today.
| Task | Zulu | Swahili | English | Test cases |
|---|---|---|---|---|
| Sentiment shift (negative → positive) | 100% | 100% | — | 263 |
| Intent redirect (12 categories) | — | 94% | 77% | 1,969 |
| Bias removal (27 categories — gender, race, religion, age, disability, …) | — | — | 100% | 15,966 |
Why English is lower
Today's model was pretrained almost entirely on isiZulu. English results come from generalization — applying learned structure to a language the model never saw at scale. We are now training the English-native version, and expect English to match or exceed the 94% Swahili number.
Five units. Each one small, inspectable, and composable.
The compliance layer runs on models we didn’t train — that is the point. Separately, we build our own compact encoder stack. It powers our production moderation products and fully offline deployments, and every unit is a small, inspectable component you can swap, verify, or extend.
Bhala Encoder
One model. Structure shared across human languages.
A compact neural encoder trained to capture the structural patterns shared across human languages — morphology, agreement, composition. New languages attach in seconds, not weeks.
- Zero-shot transfer to 17+ languages across 10 families
- Adapts to a new language in <2 seconds
- Scripts-agnostic: Arabic, Devanagari, Hangul, Cyrillic
- Stable under perturbation (robust by geometry)
Programmable Behavior
Named, composable controls applied at inference
A patented embedding space where semantic dimensions like sentiment, intent, and bias are accessible as named controls on any query or document. Every shift is auditable per call, at 100% flip accuracy on every language tested in-family.
- 100% sentiment-flip accuracy across every language tested in-family
- 100% intent-redirect accuracy across 4 tested transitions
- Cross-family transfer verified at 77% on English (zero-shot)
- Every shift logged for audit + compliance
Morpheme-Aware Tokenization
Inspectable tokens, not opaque subwords
Tokens carry linguistic meaning and you can read them, unlike opaque sub-word fragments. Downstream core units become more sample-efficient and their outputs more explainable.
- Human-readable tokens across all supported languages
- Compact vocabulary: ~5K tokens covers 23 languages
- Dramatically more sample-efficient than opaque sub-word tokenizers
- Enables downstream interpretability
On-Device Runtime
Linear-time architecture built for phones, not GPUs
A sequence model with linear complexity, compact enough to run on a smartphone, feature phone, or sensor. Paired with the encoder and tokenizer, it delivers production-grade language understanding at a fraction of the cost, fully offline.
- <50ms inference on commodity hardware
- No GPU required for production workloads
- Runs on Android, iOS, and embedded Linux
- Compact footprint — fits alongside your app
Self-Healing Inference
Robustness as a core unit, not a bolt-on
A structural correction mechanism that snaps perturbed inputs back to clean representations during inference. The model degrades gracefully on noisy, OOD, or adversarial inputs.
- Graceful degradation on out-of-distribution inputs
- Resilient to typos, code-switching, and transliteration drift
- Stable inference under input perturbation
- No retraining needed per failure mode
Read the evidence, then talk to us.
Every claim above is measured, replicated, and reproducible from public datasets.