Before We Build Agents, We Need to Fix the Systems That Already Cannot See Us
Everyone is chasing the next wave of AI.
Autonomous agents.
Self-directed intelligence.
Systems that take a goal and complete it without supervision.
I understand the excitement.
People want efficiency.
They want magic.
They want machines to feel like collaborators instead of tools.
But I keep returning to a question that sits heavier each week:
If today’s AI cannot recognize queer and BIPOC people accurately, what do you think will happen when tomorrow’s AI begins making decisions about us without a human in the loop?
We talk about “acceleration” as if speed is a virtue. Speed only helps if the foundation is sound. Right now, the foundation is cracked.
Most of the systems being built today do not understand our language, our cultural signals, our creative forms, or the way identity moves in our communities. They treat our ways of speaking as errors. They treat our ways of presenting ourselves as inconsistencies. They treat our emotional expressions as instability. They treat our humor as risk.
These misunderstandings are not neutral.
They become classification decisions.
They become risk scores.
They become moderation outcomes.
They become policy.
And the people who feel these misreadings most intensely are the same people who have always felt the sharp edge of misinterpretation: queer folks, Black and Brown communities, immigrants, trans people, diaspora families, and anyone whose identity is shaped by movement rather than stability.
This is the part the AI industry has not internalized. Misinterpretation is not a minor flaw. Misinterpretation is a structural failure. And once it is baked into a model, it spreads through every system that touches that model.
Content moderation.
Identity verification.
Fraud detection.
Personalization.
Emotion recognition.
Safety classification.
Hiring algorithms.
Healthcare triage.
Travel security.
Customer service routing.
Recommendation feeds.
All of these systems rely on models that were trained to understand people who exist within a narrow social template.
That template does not match us.
And because it does not match us, the system treats us as anomalies.
The problem is not that AI gets things wrong.
The problem is that AI turns those mistakes into truth.
This work did not begin as an academic exercise.
It began as a personal frustration that turned into a pattern I could no longer ignore.
I kept seeing the same issue repeated in every AI system I touched.
It was not bias in the emotional sense.
It was not malice.
It was not intentional harm.
It was structural.
The models were not built to hold identity that shifts.
Identity that blends.
Identity that contradicts itself.
Identity that is shaped by history and survival rather than clear categories.
Queer and BIPOC people do not experience identity as a box to check.
We experience it as a set of tools we use to move through different spaces safely.
We switch registers.
We switch languages.
We switch gender cues.
We switch emotional tones.
We speak in code when safety requires it.
We speak loudly when the room finally gives us space
These shifts are not inconsistencies. They are intelligence, and AI does not understand this yet.
That realization is what led to the Knox AI Empathy System. I built it because I could not find anything else that addressed the problem at the level where the harm actually begins: interpretation.
The tech industry keeps reaching for bigger models, more guardrails, more policies, more audits, more promises. None of that matters if the underlying architecture cannot read us correctly.
You cannot fix misinterpretation with more rules.
You fix misinterpretation by changing how the system perceives in the first place.
We should not be designing autonomous agents until we correct the systems that already treat us as statistical noise.
People are excited about giving AI goals.
Tasks.
Autonomy.
But autonomy is dangerous when the intelligence beneath it is distorted.
If the model sees queer masculinity as aggression, an agent will act on that.
If the model sees AAVE as hostility, an agent will act on that.
If the model sees trans existence as inconsistency, an agent will act on that.
If the model sees diaspora English as confusion, an agent will act on that.
If the model sees Indigenous knowledge as unverifiable, an agent will act on that.
This is not science fiction.
This is not a future threat.
This is already happening in multiple industries.
The only reason it is not a crisis is that humans are still in the loop. Agents remove the loop.
That is why this work matters now.
Not because it is elegant.
Not because it is clever.
Because it is timely.
Because the conversation about AI is moving faster than the conversation about identity.
Because we cannot allow automated systems to inherit the structural misreadings we have been fighting against for decades.
The truth is simple: Before we teach AI how to act, we need to teach it how to understand.
This is the purpose of the white paper.
This is the purpose of the work.
This is the purpose of finally using my voice without shrinking it.
I wrote the Knox System because the field needed an architectural intervention.
I wrote it because queer and BIPOC identity deserves accuracy, not tolerance.
I wrote it because misinterpretation is the ground zero of harm.
I wrote it because the industry is accelerating toward autonomy without correcting the basic failures that already exist.
Mostly, I wrote it because silence felt irresponsible.
I do not expect everyone to agree with me.
I only need people to understand the stakes.
If AI continues to learn from systems that misunderstand us, it will automate that misunderstanding at scale.
But if we intervene now, we can build a future where our identities are not treated as errors to be corrected, but as truth worth interpreting accurately.
That is why this work exists.
And why it needs to exist right now.
