https://ethical.institute/principles.html
Ethical AI Network
The Ethical ML Network (BETA) is a global network of volunteers consisting of 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.
The members of the Ethical AI Network contribute to the open source workstreams at the institute, which include our 8 Principles for Responsible ML, the AI RFX Framework, and our open source libraries and frameworks
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. Data risk awareness
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.
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. Data risk awareness
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.
