»How do algorithmic models become biased?«
2019-06-04, 14:30–14:55, Hall 2 (PyData)

In this talk, we will walk through a step by step example of building a prediction algorithm, focusing on areas where bias could be inadvertently introduced. Then, we'll look at some real examples and solutions.

In this talk, we will walk through the steps of how to build an algorithm to predict property prices from a dataset of property listings, focusing predominantly on finding the right features to include in building the model. Then, we will understand where in the feature engineering process we start introducing bias into our algorithms, and what are the ramifications of this if the model were to be deployed in the real world. Using the prediction algorithm as a framework to look at each step of the building process, we will also look at real-world examples of when certain decisions have led to unequal and biased results.