fairlearn/fairlearn

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Interpretability and explainability of data and machine learning models

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

Extra blocks for scikit-learn pipelines.

AIDE: an LLM agent for machine learning engineering - the research Weco grew out of. Referenced in OpenAI MLE-bench.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

MLE-bench is a benchmark for measuring how well AI agents perform at machine learning engineering

A game theoretic approach to explain the output of any machine learning model.