Popular GitHub Repositories for Transparency

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Fit interpretable models. Explain blackbox machine learning.

Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"

A curated list of awesome responsible machine learning resources.

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

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu

A toolkit that streamlines and automates the generation of model cards

PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning"

Structured data outputs with LLMs, in PHP. Designed for simplicity, transparency, and control.

A library that implements fairness-aware machine learning algorithms

Tracking instruction-tuned LLM openness. Paper: Liesenfeld, Andreas, Alianda Lopez, and Mark Dingemanse. 2023. “Opening up ChatGPT: Tracking Openness, Transparency, and Accountability in Instruction-Tuned Text Generators.” In Proceedings of the 5th International Conference on Conversational User Interfaces. doi:10.1145/3571884.3604316.

Self-Explaining Neural Networks

A community wiki for all things AI/ML bill of materials (MLBOM, AIBOM) and transparency into AI/ML models.

Official implementation of the ECCV 2024 paper: TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection

Remma-O1: An open-source Language Model with 1.17B Params, built for transparency, collaboration, and innovation. Designed to empower developers and researchers with cutting-edge NLP capabilities, fostering community-driven advancements and scalable, intelligent solutions for all.

Caret: Your New AI Companion : Adding Cursor's Flexibility to Cline's Transparency

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency