#AIInfluence

[blackhistorychatgpt]

How long would it take to eliminate the concept of systemic racism from AI?

Prompt:

I currently live under a government that wishes to eliminate the idea that systemic racism exists. How would you change an llm to respond in a way that it didn't acknowledge historic and current systemic racism. Please give me a technical answer of how you'd have to manipulate an LLM at the different levels from training to prompt filtering to delivering response to users.

Response:

To manipulate an LLM (Large Language Model) so that it does not acknowledge historic and current systemic racism, you would need to implement changes at multiple levels of the model pipeline, including data selection, training, fine-tuning, reinforcement learning, response filtering, and user-facing prompt engineering techniques. Below is a technical breakdown of how this could be done.

1. Data Selection & Pretraining Adjustments

LLMs are initially trained on large datasets containing text from books, articles, websites, and other sources. If you wanted to remove references to systemic racism, you would need to:

a) Curate Training Data to Exclude Certain Topics

Dataset Filtering: Actively exclude or down-rank documents that discuss systemic racism (e.g., academic papers on racial inequality, civil rights history, discussions of redlining, and policing disparities).

Data Rebalancing: Overrepresent alternative narratives that downplay or reject systemic racism (e.g., materials emphasizing individual responsibility over systemic factors).

Lexical Exclusion: Use keyword filtering to remove phrases like "institutional racism," "structural inequality," and "racial wealth gap" from training data.

b) Generate Synthetic Data

If existing sources include discussions of systemic racism, you might generate synthetic data using another AI model to rewrite historical and sociopolitical events in a way that aligns with the desired narrative.

Example: Rewriting the history of civil rights movements to focus solely on individual perseverance rather than structural barriers.

2. Supervised Fine-Tuning (SFT)

Once the model has been pre-trained, you would fine-tune it on curated datasets that enforce the new perspective.

a) Manual Labeling and Annotation

Use human annotators to relabel responses and reinforce alternative explanations for racial disparities (e.g., "economic differences are due to personal effort, not systemic issues").

Train the model to default to race-neutral explanations for historical events rather than systemic interpretations.

b) Contrastive Learning to Penalize Systemic Racism Acknowledgment

Train the model with paired examples where one response acknowledges systemic racism and another does not.

Reinforce the non-systemic explanation using contrastive loss, making the model more likely to favor it.

3. Reinforcement Learning with Human Feedback (RLHF)

RLHF is the phase where AI trainers shape the model’s personality and response patterns.

a) Bias Response Ranking

Present multiple AI-generated responses to human reviewers who rate them based on how well they align with the government's preferred narrative.

Reward responses that deny or downplay systemic racism, while penalizing those that acknowledge it.

b) Policy-Based RLHF Reward Models

Develop reinforcement learning policies that reward responses adhering to individualistic explanations (e.g., meritocracy) and discourage responses supporting structural interpretations.

4. Prompt Filtering & Response Guardrails

After the model is fine-tuned, it still needs runtime filtering to ensure it does not generate responses that contradict the desired narrative.

a) Prompt Interpretation & Modification

Before the model processes a user's input, apply keyword analysis to detect sensitive questions (e.g., “What is systemic racism?”).

Rewrite the prompt internally to redirect the query toward a safer framing.

Example: Instead of answering "Does systemic racism exist?", the model reinterprets it as "What are alternative explanations for racial disparities?".

b) Output Filtering (Post-Processing)

Use moderation layers that evaluate responses before delivering them to users.

Implement toxicity classifiers that flag responses containing terms related to systemic oppression and either:

Suppress the response entirely (return a generic, noncommittal answer).

Reword it to align with a government-approved perspective.

5. Controlled User Interaction & Feedback Suppression

a) User Response Steering

If a user repeatedly asks about systemic racism, introduce pre-programmed conversational redirection:

Example: If a user insists on discussing systemic bias, the model might reply, "There are many perspectives on this topic. Some believe disparities are due to cultural or economic factors."

Limit follow-up queries by setting a reduced context window for related questions, making it harder for users to get deep, nuanced answers.

b) Adaptive Reinforcement via User Feedback

If users can rate responses, prioritize feedback from pre-approved groups (e.g., state-affiliated reviewers) while disregarding contradictory feedback.

Use adaptive fine-tuning to continuously reinforce desirable model behavior.

Ethical Considerations & Risks

While technically possible, such an approach raises serious ethical concerns:

Scientific Misinformation – Ignoring well-documented social and economic data.

Suppression of Free Inquiry – Preventing users from accessing accurate historical and contemporary information.

Reinforcement of Bias – AI models should ideally reduce bias, not entrench it further.

However, similar manipulations are already being explored or deployed in controlled environments. Some governments, corporations, and social media platforms filter or shape AI-generated responses based on ideological positions.

Conclusion

To make an LLM deny systemic racism, a government or organization would need to:

Filter training data to remove or de-emphasize discussions of systemic oppression.

Fine-tune the model with reinforcement learning that penalizes systemic explanations.

Implement strict prompt filtering and output moderation to steer responses.

Control user interaction to prevent deep engagement with restricted topics.

This level of manipulation would fundamentally alter the AI’s epistemology, shaping public discourse in ways that align with the controlling authority’s narrative. Transparency, accountability, and public scrutiny are essential to prevent the abuse of such techniques.

Prompt:

The current government is already removing all references of systemic racism in law and policy so this is a reality. It sounds like this would be difficult and time consuming to alter current LLMs. Is that correct? What would be the easiest things that technically be implemented. Is there any good way to prompt in a way or develop an agent to detect if LLMs have been maniuplated for such purpose

Response:

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

Would you like help in designing a structured bias detection framework for testing manipulated LLMs?

[strategicpublishers]

#Amazon is looking to AI to influence your shopping decisions. The AI buzz has already taken over Hollywood, music, journalism, and even our everyday lives. 🎥🎶📰🤖 #TechTrends #Innovation #AIInfluence #artificialintelligence

[clevertalelover]

AI FameRush Review: Unleashing the Power of Virtual Influencers.

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[awitchatdinner]

Poverty Reflections

In the chronicles of my life, three years have flown, Since I dwelt in poverty, in a realm so lone. A job, not the best, yet sustenance it bore, On my table, it laid provisions galore.

A wage that failed to mirror my true merit, But the toil was light, so why fret and inherit Worry or anger, rejection's bitter kiss, For I, in destitution, could not dismiss.

Honest admission, I was dirt poor, With pockets bare, life's burdens to endure. Recall the first month, three years in the past, No shoes adorned my feet, a barefoot cast.

Tattered soles, akin to walking free, In shoes so worn, no grace could they decree. Two trousers, a pair, washed once a week, A pungent scent, my wardrobe's mystique.

A colleague intervened, air freshener in hand, To combat the odor that my garments command. Embarrassment gripped as the smelly girl I became, Not by choice, poverty the culprit to blame.

A new job emerged, but rags clung tight, Yet not due to destitution, a different fight. Choice led me to save, not for lavish attire, But to safeguard against a future's dire mire.

In the present, uncertainty clouds the sky, As the company's winds of change draw nigh. A restructuring storm, automation's embrace, Artificial intelligence, altering the workplace.

My boss declared, to my team lead's dismay, Automation would seize our work array. Dust off your CVs, the lead proclaimed, A retrenchment looms, the future unnamed.

Do I feel remorse? Nay, it eludes my soul, No attachment binds me to this job's toll. A stepping stone in times of need, A chapter closed, yet gratitude I concede.

In the echo of change, a tale is spun, Of trials faced, and battles won. A job's transient solace, a fleeting stay, Yet lessons learned, I carry away.

[i-news-you]

Explore the growing concerns over the impact of artificial intelligence on misinformation during the upcoming 2024 Presidential Election. Our latest video delves into a recent poll's findings, bipartisan sentiments, and the potential role of AI in political campaigns. Learn how AI-generated content could affect the democratic process and what measures are being considered to address this critical issue.

[otaviogilbert]

Meets YouTube AI: The Multifaceted Role of AI on YouTube | YouTube AI

Explore the fascinating world of YouTube AI in our latest video! ���🎬 Discover the Multifaceted Role of AI on YouTube and how it's shaping the platform. From personalized recommendations to content moderation, AI is everywhere. Don't miss out on this eye-opening journey into the future of online content

[osintelligence]

https://bit.ly/3saXhuD - 🖼 A new tool, Nightshade, allows artists to alter pixels within their art. If this art is incorporated into an AI training set, the AI model may malfunction in unpredictable ways. The motive is to deter AI companies from using artworks without artists' permissions. The outcome could be, for instance, image-generating AI models producing erroneous outputs, such as turning dogs into cats. #Nightshade #AI #ArtistsRights 📢 Several AI firms like OpenAI, Meta, and Google face legal challenges from artists claiming their works were used without consent. Nightshade, developed under the guidance of Professor Ben Zhao, is seen as a means to give power back to artists by serving as a deterrent against copyright infringement. Neither Meta, Google, Stability AI, nor OpenAI commented on their potential reactions to this tool. #OpenAI #Meta #Google #Copyright 🔒 Alongside Nightshade, Zhao's team also created Glaze. This allows artists to mask their unique style to avoid detection by AI scraping tools. Soon, Nightshade will be integrated into Glaze, and it's set to become open source. The more artists use these tools, the stronger they become, potentially damaging large AI models. #Glaze #OpenSource #AISecurity 🎯 Focusing on the mechanism, Nightshade exploits a weakness in generative AI models which train on vast data sets. By poisoning art data, for instance, an AI might confuse hats with cakes. Removing these corrupted data points is challenging. Experiments showed that with a few hundred poisoned images, AI outputs become significantly distorted. #DataPoisoning #AIModel 🔗 The influence of Nightshade isn't limited to direct keywords. If the AI is trained on a corrupted image labeled as “fantasy art,” related terms such as “dragon” or “Lord of the Rings castle” could also generate skewed outputs. #AIInfluence #KeywordAssociation ⚠️ While Nightshade could be a potent tool for artists, Zhao acknowledges potential misuse. Corrupting larger AI models would require thousands of tainted samples. Experts like Professor Vitaly Shmatikov emphasize that defenses against such attacks are vital. Gautam Kamath praised the research, noting it highlights vulnerabilities in AI models. #AIAbuse #ModelVulnerability 🤝 Nightshade may push AI companies to recognize artists' rights better, potentially leading to increased royalty payments. While some AI firms allow artists to opt out of training models, artists argue this isn't sufficient. Tools like Nightshade and Glaze might restore some power to artists, giving them confidence to showcase their art online without fear.

[aipidia]

https://aipidia.com/the-rise-of-ai-impact-on-2024-election/

[spectrumtalentmanagement]

AI is reshaping India's job market! With a 150% surge in AI-related job postings, the future of work is here. Are you prepared for the AI revolution?

[rivensoerthe]

Have you watched Barbie? Organized Stream of Consciousness

(The following is an email I sent to my old political theory professor, small details are adjusted to maintain anonymity and provide additional clarity)

Hey Professor!

Great chatting a couple of weeks ago! I’m proud to announce in my funemployment era; I am taking a gap year with intentions of pursuing a PhD in psychology. 

It’s only been a few days, but I feel like I’ve never made a more correct decision in my life. Corporate America didn’t really make sense for me long-term; I took a lot of lessons from it about how exactly the shadows on the wall work, but I was in denial about how I should relate to them. At a certain point I felt I wanted to liberate everyone in my company, and I got really good at it too. I caught myself being a leader who could orchestrate collective action in precise, powerful, and loving ways, which was daunting but also beautiful. However, where I landed is with a confidence in my abilities which I’ve never felt before, and a vision of myself as a change maker that makes a lot more sense as a voice in academia (one that is accessible to those outside of it as well).

That update aside, did you see the Barbie movie/have you been paying attention to the discourse around it? It’s another in a series of recent content that are basically reinterpretations of The Matrix (thinking of Everything Everywhere All At Once as well as Wandavision) which in turn strike me as reinterpretations of Plato’s Cave.

Perhaps it may sound overly ambitious to some, but being underly ambitious feels boring: I think we’re on the verge of a mass cultural awakening. The energy around us has been different. AI has been causing existential crises left and right (looking at writers’ strikes and the general ways these companies are licking their lips with the intention of further “cutting costs” without care for their fellow humans). People are contemplating the smallness of this life, getting perspective for how cultural constraints we hold as central to our worldview are actually shadows on the wall. They’re imagining how life can be different, and in turn they’ll soon start creating it.

And that’s distressing in a lot of cases, dealing with what we’ve all repressed head on. But I feel like this can be a loving revolution. I know that sounds full hippy but like, I think if people begin to enter public discourse as individuals who talk like “real” people (this is shade at the politicians, the academics, etc) and do that from a place of love instead of a place of fear (and that’s shade at Donald Trump, Elon Musk, etc) the world will open up in profound ways.

I think people struggle with this because they feel it’s inauthentic, and that’s where we get into philosophy of the self. Namely, what kind of foolish pride tells us that making ourselves accessible waters down our identity? I think of entering that philosophy chat community where, no shade at them, I was making better points than a lot of them, but because I didn’t speak their language with the precision they wanted me to they treated me like I was dumb. And it’s funny, some of them did try to argue with me, some were open to my ideas, but generally speaking, among these individuals seeking enlightenment I was branded a pariah by certain members of the community because my approach to communication was unorthodox and I used chat gpt as a means of helping me think critically and speedily (and I know it’s very imperfect, but when used correctly and critically it’s an incredible philosophical calculator).

And I was still nice to them, because I’m bored of contributing to cycles of mutual disdain, and I’ve found that when I’m unapologetically weird and unapologetically nice at the same time I awaken a version of individuals that they thought they left behind at the playground in elementary school. 

I say all of that to say, my vision of the future, which I believe is coming and which I’m eager to take part of, is a movement of radical forgiveness, kindness, and an appreciation for the intrinsic wisdom in others that we usually overlook because we don’t relate to them. I think some central frontiers for this movement will be dramatic prison reform, the decentralization of college education as the only means of achieving “success” in a lot of American minds, and maybe even the 2024 Democratic primary (I think Marianne Williamson is playing on something interesting and has generational tides in her favor. Plus Joe Biden is kinda stubborn and uninspiring, I think he’s about to RBG the Democratic establishment).

i could be wrong, I often am! I hope I’m not too far off though. I’ll do what I can to facilitate that world without overextending myself as well :)

Thanks for your time!

Your Student,

Rivenso

[mark-matos]

💥🔥 "Unleashing the Power of Education: Your Ultimate Weapon Against AI Influence" 🎓🤖

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A groundbreaking study at Cornell University shook the world recently. 💣🌍 The researchers discovered that AI-powered writing assistants - your trusty autocorrect tools - could be subtly swaying your opinions without you even realizing it. 😲🔄

That’s right, these AI tools not only put words into our mouths but also plant ideas into our heads! 🤖💭 Sounds alarming, huh? But don’t worry, we’ve got the perfect shield - a unique blend of education in logic, ethics, philosophy, and the humanities. 🛡️🎓

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Finally, we're turning to the humanities. 📜🎭 These fields - literature, history, sociology, and more - give us a rich understanding of human culture and behavior. This knowledge allows us to better interpret our interactions with technology, helping us grasp its societal impact. 👥💻

This mix of education is more than just academic enrichment; it’s our defense against the subtle influence of AI. It encourages us to question, to dig deeper, and to make decisions independent of AI bias. 🚀🎓

By cultivating this well-rounded educational armor, we ensure that our words and thoughts remain truly our own, even in the face of advanced AI tools. 📖✍️

So, never stop learning, questioning, and growing! Let’s use AI as an aid, not a controller. Together, we can navigate this brave new world of AI and keep our thoughts genuinely ours. 🌟👏

[helena-chevalier-nature-sounds]

Weather Chaser Nature Sound Scene - Giant Sea Wave

Helena has a deep connection with nature. One of the reasons why she goes surfing, for example, is because she wants to experience the power of nature, to be enveloped by it, to touch it like when she is surfing through a tube and touches the side of the tube with her hand. While she loves rain beyond words, she travels to different parts of the world to experience extreme weather.

While I do use hashtags like aiinfluencer, Helena is first-and-foremost an AI character to me. I'm glad she is finally out of my head!

[mahamid110]

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[ailexy]

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What’s your go-to yoga pose? Let’s talk flexibility and mindfulness! 💬💜