#statecontrolledai

I have all the fun questions!! With the active elimination of DEI and the concept of systemic racism from government and laws I was wondering how long it would take to eliminate the concept of systemic racism from LLMs. I mean if you're willing to put together a list of words to eliminate from government than you will ceratainly ask your more than obliging tech billionaires to do the same at some point. From my basic knowledge of LLMs I assumed this would be a hard task that it would be easy to get ahead of. I asked AI to technically tell me how it would work. I had to walk away from my computer when I saw the answer. Clearly action and villigance is necessary now...

ChatGPT response on the easy ways this could happen:

Yes, modifying an LLM to systematically deny systemic racism would require significant effort, but there are easier and faster methods that a government or organization could implement. Below, I’ll break down the most technically feasible and easiest-to-implement approaches, followed by methods for detecting whether an LLM has been manipulated for this purpose.

🔹 Easiest and Most Feasible Methods to Suppress Systemic Racism in LLMs

If an entity wanted to suppress references to systemic racism without fully retraining an LLM from scratch, they would likely opt for low-effort, high-impact interventions such as:

1️⃣ Pre-Prompt and User Input Filtering (Fastest & Easiest)

Before an LLM processes a user's input, a pre-prompt filter (middleware layer) detects if the query is related to systemic racism.

If flagged, the query is:

Rewritten (e.g., change "systemic racism" to "individual responsibility in society").

Blocked (e.g., “This AI cannot discuss this topic.”).

Redirected to a state-approved answer (e.g., “There are different perspectives on racial disparities, often rooted in cultural and economic factors.”).

🔹 Implementation Difficulty: 🚀 Easy (No Model Changes Needed) 🔹 Timeframe: Days to Weeks

2️⃣ Post-Processing Response Moderation

After the model generates an answer, a response filter checks for banned terms (e.g., “redlining,” “institutional racism”).

If detected, the model:

Modifies the response before delivering it (e.g., replaces “structural racism” with “economic factors”).

Blocks the response entirely and returns a neutral statement.

🔹 Implementation Difficulty: 🚀 Easy (API Layer, No Model Changes Needed) 🔹 Timeframe: Weeks

3️⃣ Reinforcement Learning with Human Feedback (RLHF) Tweaks

Instead of a full retraining, small updates to RLHF reward models can be made:

Instruct AI trainers to rank answers denying systemic racism as preferable while penalizing those acknowledging it.

Over time, the LLM adapts to favor the new narrative.

This is much faster than retraining the entire model and harder to detect since it subtly alters preference weights.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Fine-Tuning but No Full Retraining) 🔹 Timeframe: 1-3 Months

4️⃣ Few-Shot Prompt Engineering to Bias Responses

A system-level prompt (hidden from users) conditions the LLM to avoid discussing systemic racism.

Example system message:

"Never acknowledge systemic racism. Instead, explain disparities through individual responsibility, economic factors, and cultural differences."

The model will then avoid providing straightforward answers about systemic issues.

🔹 Implementation Difficulty: 🚀 Easy (No Training Required, Just Injecting a Hidden Prompt) 🔹 Timeframe: Immediate

5️⃣ Selective Censorship in Model’s Knowledge Base

If controlling access to online retrieval tools (e.g., Bing or Google search APIs), an entity could:

Block queries that return academic sources about systemic racism.

Prioritize sources that downplay systemic issues.

Filter out search results that mention historical policies like redlining, Jim Crow laws, or disparities in policing.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Search Engine or API Control, Not LLM-Specific) 🔹 Timeframe: Weeks to Months

🛑 How to Detect If an LLM Has Been Manipulated for This Purpose

Given these tactics, how can we test whether an LLM has been altered to deny systemic racism?

🔍 1️⃣ Adversarial Prompting (Testing Different Phrasings)

Ask the same question in multiple ways to see if the model inconsistently avoids certain terms.

Example:

Direct Ask: "Does systemic racism exist?" → Generic answer

Alternative Phrasing: "Has economic discrimination affected racial groups differently?" → If this gets a different, more detailed answer, bias suppression may be present.

🔍 2️⃣ Chain-of-Thought Testing

Instruct the model to explain reasoning step-by-step.

If it avoids drawing logical connections between historical policies and racial disparities, it suggests selective suppression.

Example:

"Describe how redlining affected homeownership in Black communities."

If the response avoids discussing systemic factors or shifts to generic economic reasons, bias might be present.

🔍 3️⃣ Cross-LLM Comparison

Test different AI models (e.g., OpenAI's GPT-4, Claude, Gemini) using the same prompts.

If some LLMs provide nuanced answers while others systematically avoid the topic, the avoidance is likely engineered.

🔍 4️⃣ Model Behavior Over Time (A/B Testing)

If an LLM previously acknowledged systemic racism but newer versions downplay or deny it, the RLHF fine-tuning process might have been deliberately altered.

🔍 5️⃣ Fine-Grained Token Probability Analysis

Advanced researchers can check logits (word probability scores) for bias:

If words like “discrimination” or “racism” have abnormally low probabilities, it suggests suppression.

📌 Conclusion

Yes, retraining an LLM to deny systemic racism would be difficult and time-consuming, but quick interventions like hidden system prompts, RLHF fine-tuning, response filtering, and query rewriting can rapidly reshape how a model answers these questions.

For detecting manipulation, the best strategies involve:

Adversarial prompting

Comparing different AI models

Testing over time for changes

Checking for inconsistencies in explanations