A Quick Introduction to Machine Learning

Building a responsible ML process

Machine learning processes are typically developed using open source code and code written in house. All machine learning processes should meet certain quality standards no matter who develops them or what purpose they’re used for. Quality standards include the following aspects: Rigorous – in terms of the scientific methods used and the testing they go through. Responsible – in terms of how they’re used and what they’re used for. Trustworthy – in terms of sound implementation. Ethical – both in terms of the data and the algorithms themselves. To ensure that machine learning processes at Statistics Canada meet these expectations, we’ve developed a framework for responsible machine learning processes.

A responsible machine learning process ensures:

• Respect for people by ensuring there’s no bias or discrimination in the learning data. Everyone is treated fairly.
• Respect for data that protects privacy of people and businesses, ensures security of data through all processing steps, and protects confidential information to prevent disclosure.
• Sound application that ensures transparency and reproducibility of both the process and the results.
• Sound methods that are compliant with quality guidelines and uses appropriate metrics to measure accuracy and performance.