Data Ethics Workbook for Public Sector

By UK Department for Digital Culture Media & Sport

The Data Ethics Workbook will help you decide how you to align your work with the Data Ethics Framework principles. It will help you design an implementation plan for managing high quality results and mitigating risks...

An Ethical Toolkit for Engineering/Design Practice

By The Markkula Center for Applied Ethics

The tools below represent concrete ways of implementing ethical reflection, deliberation, and judgment into tech industry engineering and design workflows...

AI RFX Procurement Framework

By The Institute for Ethical AI & Machine Learning


The AI-RFX Procurement Framework is a set of templates that were put together by domain experts to support industry practitioners looking to procure AI systems...

Awesome production machine learning

By The Institute for Ethical AI & Machine Learning


This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning...

Designing Ethical AI Experiences: Checklist and Agreement

By Software Engineering Institute – Carnegie Mellon University


This document can be used to guide the development of accountable, de-risked, respectful, secure, honest, and usable artificial intelligence (AI) systems with a diverse team aligned on shared ethics...

AI system ethics self-assement tool

By Smart Dubai Government Office


Dubai’s Ethical AI Toolkit has been created to provide practical help across a city ecosystem. It supports industry, academia and individuals in understanding how AI systems can be used responsibly. It consists of principles and guidelines, and a self-assessment tool for developers to assess their platforms...

Datasheets for Datasets

By Microsoft


The machine learning community has no standardized way to document how and why a dataset was created, what information it contains, what tasks it should and should not be used for, and whether it might raise any ethical or legal concerns. To address this gap, we propose the concept of datasheets for datasets...

AI Explainability 360 Open Source Toolkit



This extensible open source toolkit can help you comprehend how machine learning models predict labels by various means throughout the AI application lifecycle. We invite you to use it and improve it...

Deon Checklist for data science projects

By Driven Data


deon is a command line tool that allows you to easily add an ethics checklist to your data science projects. We support creating a new, standalone checklist file or appending a checklist to an existing analysis in many common formats...

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing


In this paper, we introduce a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development life-cycle...

Debiasing Representations by Removing Unwanted Variation Due to Protected Attributes


We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficient than known approaches. Further, we show that this approach leads to debiased representations that satisfy a first order approximation of conditional parity. Finally, we demonstrate the efficacy of the proposed approach by reducing racial bias in recidivism risk scores.