Institute for Ethical AI & Machine Learning
Developing practical frameworks and open-source tools for responsible machine learning development, deployment, and monitoring.
A research centre that develops frameworks and tools for responsible AI development, deployment, and monitoring. Focuses on practical, industry-applicable approaches to AI ethics through open-source tools and standards.
AI areas they serve
Areas of Focus
Responsible machine learning
Explainable AI
AI model monitoring
ML ethics frameworks
Open-source AI ethics tools
AI governance implementation
Practical AI ethics
AI Impact Areas
Upcoming Goals
Continue developing open-source responsible AI tools
Expand the Responsible ML Principles framework
Collaborate with industry and standards bodies on practical AI ethics implementation
Grow the community of responsible AI practitioners
Founded by Alejandro Saucedo
Developed "The Responsible Machine Learning Principles" — 8 principles for responsible ML
Created open-source tools including XAI (Explainable AI library), Alibi (ML model inspection), and ethical-ml frameworks
Published the ML Engineer's Guide to Responsible Development
Active contributor to AI ethics standards through partnerships with IEEE and other bodies