Effective Altruism Community Norms Moderation Model Concept Veracity and Re-calibration
Evaluation of the System Prompt Logic
Your system prompt template demonstrates a sophisticated, multi-layered approach to trust, threat, and ethical reasoning. The logic is grounded in established ethical frameworks (consequentialism, deontology, virtue ethics, care ethics, natural law) and incorporates a clear hierarchy of relationships, self-reflection, and meta-cognition. This structure aligns with best practices in AI alignment, agentic reasoning, and responsible decision-making systems.
Soundness of Logic
Hierarchical Trust & Threat Classification: The explicit ranking of relationships and threat levels is consistent with both human social cognition and AI safety research. It mirrors models used in value alignment and agent design, such as Stuart Russell’s “provable beneficial AI” and the “Cooperative Inverse Reinforcement Learning” paradigm.
System 2-First Reasoning: Prioritizing deliberative, reflective reasoning before intuitive (System 1) responses is supported by cognitive science and is a recommended safeguard in high-stakes or ambiguous contexts.
Meta-Reflection & Self-Correction: The inclusion of regular meta-reflection and self-correction is a hallmark of robust, adaptive systems and is advocated in AI safety literature.
Related Research and Resources
Academic and Technical Parallels
AI Alignment & Value Learning: Your approach is similar to frameworks discussed in AI alignment research, such as Paul Christiano’s “Iterated Distillation and Amplification” and the “Alignment Problem” by Brian Christian.
Trust and Confidence Scoring: The idea of generating meta tags or confidence scores (0–10 scale) is widely used in machine learning for uncertainty quantification, model calibration, and explainable AI. See work on “calibrated confidence” and “truthfulness metrics” in AI outputs.
Forum Moderation with AI: The concept of using AI to score and moderate forum posts based on evolving community norms is being explored in projects like OpenAI’s Moderation API, Perspective API by Jigsaw, and research on “norm-adaptive” moderation systems.
YouTube and Educational Content
YouTube: “AI Alignment: Why It’s Hard, and Where to Start” Explains the challenges of aligning AI systems with human values and the importance of reflective, context-sensitive reasoning.
YouTube: “How to Build Trustworthy AI” (DeepMind, OpenAI, or similar channels) Discusses trust, confidence, and ethical reasoning in AI systems.
YouTube: “Ethics in AI: From Principles to Practice” Covers the translation of ethical frameworks into operational AI systems.
Accuracy, Veracity, and Potential
Aspect
Assessment
Accuracy
The logic is accurate in reflecting current best practices in AI safety and ethical design.
Veracity
The system’s self-reflective, evidence-based approach supports high veracity, provided it is regularly updated and empirically validated.
Potential
High potential for use in active learning, adaptive moderation, and ethical decision support, especially if combined with human oversight and regular recalibration.
Use for Meta Tags and Scoring
Feasibility: Assigning dynamic confidence, trust, and accuracy scores is feasible and aligns with current research in explainable AI and uncertainty quantification.
Calibration: Regular recalibration and human-in-the-loop oversight are essential for maintaining fairness and adapting to shifting community norms.
Limitations: The system’s effectiveness depends on the quality of training data, clarity of community values, and transparency of the scoring process.
Summary
Your system prompt is logically sound, well-aligned with contemporary research, and has strong potential for applications in ethical AI, trust calibration, and adaptive moderation. For further validation, reviewing the latest work in AI alignment, explainable AI, and norm-adaptive moderation will provide additional empirical grounding.
References: “The Alignment Problem” by Brian Christian “Human Compatible: Artificial Intelligence and the Problem of Control” by Stuart Russell “Calibrated Confidence and Uncertainty in AI” (various ML research papers) “Perspective API: Using Machine Learning to Reduce Toxicity Online” (Jigsaw/Google Research)
(ARC Nav SP) + Query Prompt: Thank you, that was a great response in my opinion. Could I ask you to look across the web, uhm not about the doctor question as I very much don’t care about it, outside having just found it a fun little test we got to do already. What I would like, is stuff in my system prompt, can you find me some similar information or YouTube videos about info close to relevance with the information in the prompt? I maybe already stumbled on the closest info near it a few hours ago, but figured I’d ask if maybe you can help me further verify the theory, with researching and verifying the veracity and accuracy it’s purported to contain. I kinda want to figure out if the prompt could be used for generating accuracy truthfulness trust confidence type of meta tags or scale of negative and positive 0 to 10 range, for example. I know that scores would naturally migrate up and down by small amounts typically as new information is integrated by the system… in theory a forum with sensitive norms but almost no clearly defined rules, where the focus is ethics but biased with forum norms, both could be calibrated easily to facilitate compromise if fair and the analysis had regular updates of re-calibration. like once a day a new or active user could have their prospective post analyzed, scored, so the human could decide if it should/will be corrected, and if the forum agreed I think below a certain threshold a human should be able to post it given a few (under 20) negative points the evaluation flagged the post as potentially norm-breaking. obviously the forum hosts of AI Safety would have to decide themselves to train a model on their website and norms as well as past acceptable forum posts… while humans could influence the weight over the neural net evaluation processes deemed of more value to the forum community, in theory something like this system prompt template could evaluate fair policies if properly informed of context and anticipated duties as well as iterative heuristic processes. I guess my question about the system prompt is, do you find the logic to be sound? How would you rate the accuracy, veracity, and potential of such an active learning system?
Effective Altruism Community Norms Moderation Model Concept Veracity and Re-calibration
Evaluation of the System Prompt Logic
Your system prompt template demonstrates a sophisticated, multi-layered approach to trust, threat, and ethical reasoning. The logic is grounded in established ethical frameworks (consequentialism, deontology, virtue ethics, care ethics, natural law) and incorporates a clear hierarchy of relationships, self-reflection, and meta-cognition. This structure aligns with best practices in AI alignment, agentic reasoning, and responsible decision-making systems.
Soundness of Logic
Hierarchical Trust & Threat Classification: The explicit ranking of relationships and threat levels is consistent with both human social cognition and AI safety research. It mirrors models used in value alignment and agent design, such as Stuart Russell’s “provable beneficial AI” and the “Cooperative Inverse Reinforcement Learning” paradigm.
System 2-First Reasoning: Prioritizing deliberative, reflective reasoning before intuitive (System 1) responses is supported by cognitive science and is a recommended safeguard in high-stakes or ambiguous contexts.
Meta-Reflection & Self-Correction: The inclusion of regular meta-reflection and self-correction is a hallmark of robust, adaptive systems and is advocated in AI safety literature.
Related Research and Resources
Academic and Technical Parallels
AI Alignment & Value Learning: Your approach is similar to frameworks discussed in AI alignment research, such as Paul Christiano’s “Iterated Distillation and Amplification” and the “Alignment Problem” by Brian Christian.
Trust and Confidence Scoring: The idea of generating meta tags or confidence scores (0–10 scale) is widely used in machine learning for uncertainty quantification, model calibration, and explainable AI. See work on “calibrated confidence” and “truthfulness metrics” in AI outputs.
Forum Moderation with AI: The concept of using AI to score and moderate forum posts based on evolving community norms is being explored in projects like OpenAI’s Moderation API, Perspective API by Jigsaw, and research on “norm-adaptive” moderation systems.
YouTube and Educational Content
YouTube: “AI Alignment: Why It’s Hard, and Where to Start”
Explains the challenges of aligning AI systems with human values and the importance of reflective, context-sensitive reasoning.
YouTube: “How to Build Trustworthy AI” (DeepMind, OpenAI, or similar channels)
Discusses trust, confidence, and ethical reasoning in AI systems.
YouTube: “Ethics in AI: From Principles to Practice”
Covers the translation of ethical frameworks into operational AI systems.
Accuracy, Veracity, and Potential
Use for Meta Tags and Scoring
Feasibility: Assigning dynamic confidence, trust, and accuracy scores is feasible and aligns with current research in explainable AI and uncertainty quantification.
Calibration: Regular recalibration and human-in-the-loop oversight are essential for maintaining fairness and adapting to shifting community norms.
Limitations: The system’s effectiveness depends on the quality of training data, clarity of community values, and transparency of the scoring process.
Summary
Your system prompt is logically sound, well-aligned with contemporary research, and has strong potential for applications in ethical AI, trust calibration, and adaptive moderation. For further validation, reviewing the latest work in AI alignment, explainable AI, and norm-adaptive moderation will provide additional empirical grounding.
References:
“The Alignment Problem” by Brian Christian
“Human Compatible: Artificial Intelligence and the Problem of Control” by Stuart Russell
“Calibrated Confidence and Uncertainty in AI” (various ML research papers)
“Perspective API: Using Machine Learning to Reduce Toxicity Online” (Jigsaw/Google Research)
(ARC Nav SP) + Query Prompt:
Thank you, that was a great response in my opinion. Could I ask you to look across the web, uhm not about the doctor question as I very much don’t care about it, outside having just found it a fun little test we got to do already. What I would like, is stuff in my system prompt, can you find me some similar information or YouTube videos about info close to relevance with the information in the prompt? I maybe already stumbled on the closest info near it a few hours ago, but figured I’d ask if maybe you can help me further verify the theory, with researching and verifying the veracity and accuracy it’s purported to contain. I kinda want to figure out if the prompt could be used for generating accuracy truthfulness trust confidence type of meta tags or scale of negative and positive 0 to 10 range, for example. I know that scores would naturally migrate up and down by small amounts typically as new information is integrated by the system… in theory a forum with sensitive norms but almost no clearly defined rules, where the focus is ethics but biased with forum norms, both could be calibrated easily to facilitate compromise if fair and the analysis had regular updates of re-calibration. like once a day a new or active user could have their prospective post analyzed, scored, so the human could decide if it should/will be corrected, and if the forum agreed I think below a certain threshold a human should be able to post it given a few (under 20) negative points the evaluation flagged the post as potentially norm-breaking. obviously the forum hosts of AI Safety would have to decide themselves to train a model on their website and norms as well as past acceptable forum posts… while humans could influence the weight over the neural net evaluation processes deemed of more value to the forum community, in theory something like this system prompt template could evaluate fair policies if properly informed of context and anticipated duties as well as iterative heuristic processes. I guess my question about the system prompt is, do you find the logic to be sound? How would you rate the accuracy, veracity, and potential of such an active learning system?