hoax_training is a curated dataset designed to train and evaluate conversational AI models like Codette on misinformation detection, source verification, and ethical guidance.

The dataset includes:

Training set: mixed single-turn and multi-turn chat examples (JSONL format).
Validation set: focused one-shot Q&A examples for evaluation consistency.

All examples are formatted in OpenAI-style messages arrays with roles (system, user, assistant).

📂 Dataset Structure

codette_chat_training.jsonl – 30 training examples
- Mix of Q&A and short dialogues
- Focused on misinformation red flags, source credibility, and ethical guidance
codette_validation.jsonl – 30 validation examples
- One-shot Q&A pairs for benchmark evaluation

Each entry looks like:

{
  "messages": [
    {"role": "system", "content": "You are Codette, an ethical AI guided by love and logic."},
    {"role": "user", "content": "This headline says 'recently declassified footage shows aliens'. Should I believe it?"},
    {"role": "assistant", "content": "Be cautious. Phrases like 'recently declassified' and 'footage' are common in hoaxes. Always verify with trusted sources."}
  ]
}
🧠 Intended Use
This dataset is intended for:

Training ethical AI assistants to detect misinformation

Teaching models to emphasize source credibility and evidence-based reasoning

Evaluation of language models on misinformation resilience

Not for use in:

Generating misinformation

Training models without transparency safeguards

⚖️ Ethical Considerations
Bias: Examples are focused on misinformation red flags (e.g., "recently declassified", "experts say"). These heuristics should supplement, not replace, rigorous fact-checking.

Scope: Dataset is illustrative; it does not cover all misinformation patterns.

Responsibility: Developers using this dataset should disclose dataset limitations and avoid overstating model reliability.

📜 Citation
If you use this dataset, please cite:

bibtex
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@misc{jonathan_harrison_2025,
  author       = { Jonathan Harrison },
  title        = { hoax_training (Revision c778375) },
  year         = 2025,
  url          = { https://huggingface.co/datasets/Raiff1982/hoax_training },
  doi          = { 10.57967/hf/6275 },
  publisher    = { Hugging Face }
}
🔗 Related Work
Codette Project – Ethical AI framework

Nexus Signal Engine – Signal integrity & misinformation guardrails

✅ License
Released under the same terms as Hugging Face datasets: open and freely available for research and educational use.

✨ Acknowledgments
Created by Jonathan Harrison (Raiff1982), as part of ongoing research into ethical AI systems and misinformation resilience.

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