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THE INSTITUTE FOR ETHICAL AI & MACHINE LEARNING

 * Home
 * Principles ▾
   * Principles Overview
   * #1 Human Augmentation
   * #2 Bias Evaluation
   * #3 Explainability
   * #4 Reproducible Operations
   * #5 Displacement Strategy
   * #6 Practical Accuracy
   * #7 Trust by Privacy
   * #8 Security Risks
 * Institute Initiatives ▾
   * Responsible ML Principles
   * AI Procurement Framework
   * AI Explainability Framework
   * Newsletter
   * Volunteers Network
   * Machine Learning OSS Ecosystem
   * Cross-vendor GPU Computing
   * Linux Foundation Contributions
   * NumFocus Collaboration
   * NeurIPS Workshop 2022 Keynote
 * Newsletter
 * Contact us or Join


THE INSTITUTE FOR ETHICAL AI & MACHINE LEARNING

The Institute for Ethical AI & Machine Learning is a UK-based research centre
that develops frameworks that support the responsible development, deployment
and operation of machine learning systems.

We are formed by cross functional teams of volunteers including leaders in
technology, machine learning, industry, policy and academia (STEM, Humanities
and Social Sciences).


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WE HAVE A COMMITMENT TO ADVOCATE FOR THE RESPONSIBLE DEVELOPMENT OF AI


WE ARE A RESEARCH CENTRE THAT CARRIES OUT HIGHLY-TECHNICAL, PRACTICAL AND
CROSS-FUNCTIONAL RESEARCH ACROSS THE 8 MACHINE LEARNING PRINCIPLES.

WE WORK WITH INDUSTRY, ACADEMIA AND GOVERNMENTS TO DEVELOP FRAMEWORKS AND
LIBRARIES THAT ALIGN WITH OUR 4 PHASES TOWARDS RESPONSIBLE AI.



Contact us or join




THE INSTITUTE'S 4 PHASE STRATEGY TOWARDS RESPONSIBLE DEVELOPMENT OF AI



1. BY PRINCIPLE

Empowering individuals through best practices and applied principles


2. BY PROCESS

Empowering leaders through practical industry frameworks and applied guides.


3. BY STANDARDS

Empowering entire industries through our contributions to industry standards.


4. BY REGULATION

Empowering entire nations through our work.





LEARN MORE ABOUT THE 8 PRINCIPLES BELOW, OR JOIN THE ETHICAL ML NETWORK (BETA).


THE 8 MACHINE LEARNING PRINCIPLES


THE MACHINE LEARNING PRINCIPLES ARE A PRACTICAL FRAMEWORK PUT TOGETHER BY DOMAIN
EXPERTS.
THEIR OBJECTIVE IS TO PROVIDE GUIDANCE FOR TECHNOLOGISTS TO DEVELOP MACHINE
LEARNING SYSTEMS RESPONSIBLY.


BELOW ARE THE SUMMARISED 8 PRINCIPLES. FOR FULL DESCRIPTIONS GO TO THE
PRINCIPLES PAGE.


1. HUMAN AUGMENTATION

I commit to assess the impact of incorrect predictions and, when reasonable,
design systems with human-in-the-loop review processes


2. BIAS EVALUATION

I commit to continuously develop processes that allow me to understand, document
and monitor bias in development and production.


3. EXPLAINABILITY BY JUSTIFICATION

I commit to develop tools and processes to continuously improve transparency and
explainability of machine learning systems where reasonable.


4. REPRODUCIBLE OPERATIONS

I commit to develop the infrastructure required to enable for a reasonable level
of reproducibility across the operations of ML systems.


5. DISPLACEMENT STRATEGY

I commit to identify and document relevant information so that business change
processes can be developed to mitigate the impact towards workers being
automated.


6. PRACTICAL ACCURACY

I commit to develop processes to ensure my accuracy and cost metric functions
are aligned to the domain-specific applications.


7. TRUST BY PRIVACY

I commit to build and communicate processes that protect and handle data with
stakeholders that may interact with the system directly and/or indirectly.


8. SECURITY RISKS

I commit to develop and improve reasonable processes and infrastructure to
ensure data and model security are being taken into consideration during the
development of machine learning systems.





YOU CAN READ THE EXTENDED DESCRIPTIONS WITH CASE STUDIES AND EXAMPLES FOR ALL
THE PRINCIPLES AT THE PRINCIPLES PAGE.


THE AI-RFX PROCUREMENT FRAMEWORK


THE AI-RFX IS A PROCUREMENT FRAMEWORK IS A SET OF TEMPLATES TO EMPOWER INDUSTRY
PRACTITIONERS TO RAISE THE BAR FOR AI SAFETY, QUALITY AND PERFORMANCE.

THE FRAMEWORK IS OPEN SOURCE, AND CONVERTS THE THE PRINCIPLES FOR RESPONSIBLE
MACHINE LEARNING INTO A CHECKLIST.


MORE INFO AND DOWNLOAD

More information and instructions how to download at the AI-RFX Procurement
Framework Page.


RAISING THE BAR FOR AI SAFETY, QUALITY AND PERFORMANCE IN INDUSTRY

The AI-RFX procurement framework has been put together by a group of domain
experts. Its purpose is to ensure best practices in industry during the
procurement, design, devleopment and integration of machine learning systems in
industry.

The framework goes beyond the AI algorithms and provides a method to assess the
maturity of the processes and technical infrastructure around the algorithms.
The rramework consists of a request for proposal template as well as an
assessment criteria template that is based on our Machine Learnign Maturity
Model which can be downloaded at the AI-RFX Procurement Framework page.


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MORE INFO AND DOWNLOAD AT AI-RFX PAGE



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ABOUT THE INSTITUTE FOR ETHICAL AI & MACHINE LEARNING

We are a UK-based think tank that brings together technology leaders,
policymakers & academics to develop industry standards for Data Governance &
Machine Learning.

 * Contact us or Join


APPLY TO JOIN THE ETHICAL ML NETWORK (BETA)

The Ethical ML Network (BETA) is a global network of diverse engineers,
scientists, managers, leaders and thinkers that align on the 8 principles for
responsible development of machine learning, and support the 4 phases towards
responsible development of AI. The network is currently on BETA, so if you want
to join you can submit a request in the form below. This network is relevant if
you are:

 * An AI startup/scale-up founder building machine learning solutions
 * An industry professional looking to procure, develop or interact with AI
   systems
 * A professor or academic doing research related to AI, Data, Privacy and/or
   ML.
 * An engineer designing, building or maintaining machine learning systems
 * A data scientist performing analysis on big data or building statistical
   models
 * A product, project or delivery manager involved in any stage of a ML system
   lifecycle

The "Ethical ML Network (BETA)" is a play on words which reinforces our core
ethos. We believe that the only machine learning network that can be induced
with ethics in practical industrial usecases is one made out of responsible and
aligned humans who advocate for best practices during the design and development
of machine learning systems. This is reinforced in each one of the Machine
Learning Principles.


CONTACT US OR JOIN THE ETHICAL ML NETWORK (BETA)

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 * © The Institute for Ethical AI & ML. All rights reserved.

 * Principles Overview
 * #1 Human Augmentation
 * #2 Bias Evaluation
 * #3 Explainability
 * #4 Reproducible Operations
 * #5 Displacement Strategy
 * #6 Practical Accuracy
 * #7 Trust by Privacy
 * #8 Security Risks

 * Responsible ML Principles
 * AI Procurement Framework
 * AI Explainability Framework
 * Newsletter
 * Volunteers Network
 * Machine Learning OSS Ecosystem
 * Cross-vendor GPU Computing
 * Linux Foundation Contributions
 * NumFocus Collaboration
 * NeurIPS Workshop 2022 Keynote

The Institute for Ethical ML
HomePrinciples ▾Principles Overview#1 Human Augmentation#2 Bias Evaluation#3
Explainability#4 Reproducible Operations#5 Displacement Strategy#6 Practical
Accuracy#7 Trust by Privacy#8 Security RisksInstitute Initiatives ▾Responsible
ML PrinciplesAI Procurement FrameworkAI Explainability
FrameworkNewsletterVolunteers NetworkMachine Learning OSS EcosystemCross-vendor
GPU ComputingLinux Foundation ContributionsNumFocus CollaborationNeurIPS
Workshop 2022 KeynoteNewsletterContact us or Join