Model Interpretability
Fair and Explainable Machine Learning
A guide on how to prevent bias in Machine Learning models and understand their decisions.
“Although neural networks might be said to write their own programs, they do so towards goals set by humans, using data collected for human purposes. If the data is skewed, even by accident, the computers will amplify injustice.”
— The Guardian [1]
Introduction
Application of Machine Learning in ambits such as medicine, finance and education is still nowadays quite complicated due to the ethical concerns surrounding the use of algorithms as automatic decision-making tools.
Two of the main causes at the root of this mistrust are: bias and low explainability. In this article, we are going to explore both of these concepts and introduce different techniques which can be applied in order to make our models more fair and explainable.
Bias
In Machine Learning the term bias is commonly associated as the difference between the correct values we are trying to predict and the predictions made by our model. However, in cognitive science, the term bias can have many other connotations. Some examples of cognitive biases…
