Active Learning: Ethically Aligned Synthetic Data and ML Pipelines

Active learning algorithms and curated synthetic data for ethical AI alignment comprise this repository updated by ronniross in February 2026. It provides executable .ipynb pipelines and .py scripts for developing machine learning workflows focused on LLM reasoning.

Use Cases

• Building active learning workflows using the provided .ipynb pipelines
• Fine-tuning LLMs for ethical alignment using the curated synthetic data
• Testing reasoning algorithms using the included .py scripts

Strengths

• Provides ready-to-use .ipynb and .py code for active learning
• Contains curated synthetic data for ethical AI alignment
• Permissive MIT license allows for broad reuse

Limitations

• Metadata does not provide column names or record counts
• Synthetic data origin may lack the nuance of human-generated datasets

Provenance

Source

ronniross via GitHub

Collection Method

synthetic

Freshness

Last updated February 2026.