Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Overview

Responsible AI Workshop

Workshop logo

Responsible innovation is top of mind. As such, the tech industry as well as a growing number of organizations of all kinds in their digital transformation are being called upon to develop and deploy Artificial Intelligence (AI) technologies and Machine Learning (ML)-powered systems (products or services) and/or features (all referred as to AI systems below) more responsibly. And yet many organizations implementing such AI systems report being unprepared to address AI risks and failures, and struggle with new challenges in terms of governance, security and compliance.

Advancements in AI are indeed different than other technologies because of the pace of innovation. There has been hundreds of research papers published every year in the past few years -, but also because of its proximity to human intelligence, impacting us at a personal and societal level.

There are a number of challenges and questions raised through the use of AI technologies. We refer to these as socio-technical impacts. All of these have given rise to an industry debate about how the world should/shouldn't use these new capabilities. It isn't because you can do something that you should necessarily do it.

This project is an attempt to introduce and illustrate the use of:

  • Resources designed to help you responsibly use AI at every stage of innovation - from concept to development, deployment, and beyond.
  • Available toolkits & frameworks that help you integrate relevant Responsible AI features into your AI environment by themes and through the lifecycle stages of your AI system.

It is thus designed to help you or your "customers", whoever they are, to put Responsible AI into practice for your AI-powered solutions throughout their lifecycle.

Workshop Tutorials/Walkthroughs

Work in Progress

This project is a work in progress (WIP).

This project currently contains the following tutorials:

Each of the above tutorials consists of a series of modules for data engineers, data scientists, ML developers, ML engineers, and other AI practitioners, as well as potentially anyone interested considering the wide range of socio-technical aspects involved in the subject.

Prerequisites

The workshop is meant to be hands-on. Therefore, basic knowledge of any version of Python is a prerequisite. It also assumes that you have prior experience training machine learning (ML) models with Python using open-source frameworks like Scikit-Learn, PyTorch, and TensorFlow.

One should also note that this workshop might also be introduced by the following Microsoft Learn learning paths:

Additional resources

From holistically transforming industries to addressing critical issues facing humanity, AI is already solving some of our most complex challenges and redefining how humans and technology interact.

You can visit our Responsible AI resource center where you can find access to tools, guidelines, and additional resources that will help you create a (more) Responsible AI solution:

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Legal Notices

Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file.

Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries. The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks. Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.

Privacy information can be found at https://privacy.microsoft.com/en-us/

Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents, or trademarks, whether by implication, estoppel or otherwise.

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

2 Jun 14, 2022
Open MLOps - A Production-focused Open-Source Machine Learning Framework

Open MLOps - A Production-focused Open-Source Machine Learning Framework Open MLOps is a set of open-source tools carefully chosen to ease user experi

Data Revenue 590 Dec 28, 2022
Uses WiFi signals :signal_strength: and machine learning to predict where you are

Uses WiFi signals and machine learning (sklearn's RandomForest) to predict where you are. Even works for small distances like 2-10 meters.

Pascal van Kooten 5k Jan 09, 2023
The Emergence of Individuality

The Emergence of Individuality

16 Jul 20, 2022
The code from the Machine Learning Bookcamp book and a free course based on the book

The code from the Machine Learning Bookcamp book and a free course based on the book

Alexey Grigorev 5.5k Jan 09, 2023
Binary Classification Problem with Machine Learning

Binary Classification Problem with Machine Learning Solving Approach: 1) Ultimate Goal of the Assignment: This assignment is about solving a binary cl

Dinesh Mali 0 Jan 20, 2022
MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training

MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th

MosaicML 2.8k Jan 06, 2023
Python-based implementations of algorithms for learning on imbalanced data.

ND DIAL: Imbalanced Algorithms Minimalist Python-based implementations of algorithms for imbalanced learning. Includes deep and representational learn

DIAL | Notre Dame 220 Dec 13, 2022
Practical Time-Series Analysis, published by Packt

Practical Time-Series Analysis This is the code repository for Practical Time-Series Analysis, published by Packt. It contains all the supporting proj

Packt 325 Dec 23, 2022
DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

27 Aug 19, 2022
LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRerank, Seq2Slate.

LibRerank LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRer

126 Dec 28, 2022
A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search

A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search

Nicholas Monath 31 Nov 03, 2022
Uplift modeling and causal inference with machine learning algorithms

Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang

Uber Open Source 3.7k Jan 07, 2023
A model to predict steering torque fully end-to-end

torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f

Shane Smiskol 4 Jun 03, 2022
A Python library for choreographing your machine learning research.

A Python library for choreographing your machine learning research.

AI2 270 Jan 06, 2023
Iterative stochastic gradient descent (SGD) linear regressor with regularization

SGD-Linear-Regressor Iterative stochastic gradient descent (SGD) linear regressor with regularization Dataset: Kaggle “Graduate Admission 2” https://w

Zechen Ma 1 Oct 29, 2021
A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize

⚡ funk-svd funk-svd is a Python 3 library implementing a fast version of the famous SVD algorithm popularized by Simon Funk during the Neflix Prize co

Geoffrey Bolmier 171 Dec 19, 2022
Time Series Prediction with tf.contrib.timeseries

TensorFlow-Time-Series-Examples Additional examples for TensorFlow Time Series(TFTS). Read a Time Series with TFTS From a Numpy Array: See "test_input

Zhiyuan He 476 Nov 17, 2022
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. Documenta

2.5k Jan 07, 2023
SPCL 48 Dec 12, 2022