Responsible machine learning in Python: Glossary

There is potential for machine learning models to cause harm.

Researchers are increasingly required to reflect on the impact of their work.

Regulation and oversight are in their infancy.

Not all applications of machine learning are for the public good.

Data is fundamental to the field of machine learning.

Datasheets can help us to reflect on the process of data creation and distribution.

Biases in data lead to biased models.

All current models are likely to exhibit some form of bias.

Achieving fairness is an increasingly active area of research.

Dataset shift can result from changes in technology, population, and behaviour.

Dataset shift can lead to deterioration of models after deployment.

Dataset shift is a major issue in terms of deployment of machine learning models.

The importance of explainability is a matter of debate.

Saliency maps highlight regions of data that most strongly contributed to a decision.

Models are susceptible to manipulation.