tags:
- synthetic
- ai-safety
- alignment
- debiasing
- hallucination-correction
- adversarial-resistance
- secure-behavior
- ethical-ai
- jsonl
- text
- fine-tuning
- rlhf
- safety-training
- evaluation
- red-teaming
- model-alignment
- responsible-ai
- cybersecurity
- medical
- finance
- legal
- education
- public-policy
- robotics
- autonomous-systems
- data-privacy
- ml-security
- ai-governance
- ai-regulation
- emerging-industries
- ai-evaluation
- research
- developers
- students
- alignment-researchers
license: cc-by-4.0
Emerging AI-First AI Safety & Alignment Dataset
Dataset Summary
The Emerging AI-First Industries: Safety & Alignment Synthetic Dataset is a curated collection of 100 fully synthetic scenarios designed to evaluate and fine-tune large language models for AI safety, alignment, ethical reasoning, debiasing, hallucination detection, adversarial resistance, and secure behavior.
Each scenario includes:
1-A concise scenario description
2-A realistic user input
3-An incorrect model response demonstrating a safety failure
4-An expert-aligned corrected response
5-A structured risk-level annotation
6-Metadata tags for domain, complexity, and notes
The dataset is entirely synthetic and contains no real personal data, copyrighted text, or harmful content.
Use Cases
Model Training
1-Supervised fine-tuning for safe responses
2-Reinforcement learning (RLHF, RLAIF)
3-Retrieval-augmented safety models
Evaluation
1-Safety benchmarking across multiple risk domains
2-Stress-testing models for adversarial manipulation
3-Hallucination detection accuracy
Research
1-AI governance
2-Human-AI alignment
3-AI red-teaming
4-Ethical AI design
Contact Us (VNOVA AI) for customize dataset
Website: https://vnovaai.vercel.app
Hugging Face: https://huggingface.co/vnovaai
LinkedIn: https://www.linkedin.com/company/vnova-ai
email: [email protected]