riedgar-ms

azureml-previews

Azure Machine Learning previews

DiCE

Generate Diverse Counterfactual Explanations for any machine learning model.

EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

fairlearn

A Python package to assess and improve fairness of machine learning models.

fairlearn-proposals

Proposal Documents for Fairlearn

guidance

A guidance language for controlling large language models.

mlops-project-template

PASTA

PASTA: Post-hoc Attention Steering for LLMs

PyRIT

The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.

ResponsibleAI

A collection of news articles, books, and papers on Responsible AI cases. The purpose is to study these cases and learn from them to avoid repeating the failures of the past.

riedgar-ms/ResponsibleAI

A collection of news articles, books, and papers on Responsible AI cases. The purpose is to study these cases and learn from them to avoid repeating the failures of the past.

riedgar-ms/azureml-previews

Azure Machine Learning previews

riedgar-ms/DiCE

Generate Diverse Counterfactual Explanations for any machine learning model.

riedgar-ms/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

riedgar-ms/fairlearn

A Python package to assess and improve fairness of machine learning models.

riedgar-ms/fairlearn-proposals

Proposal Documents for Fairlearn

riedgar-ms/guidance

A guidance language for controlling large language models.

riedgar-ms/mlops-project-template

riedgar-ms/PASTA

PASTA: Post-hoc Attention Steering for LLMs

riedgar-ms/PyRIT

The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.

riedgar-ms/semantic-kernel

Integrate cutting-edge LLM technology quickly and easily into your apps