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.

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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.

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.



More info and download at AI-RFX page

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)