Creating search and recommendation algorithms that are ef-ficient and effective has been the main objective for the in-dustry and the academia for years. However, recent research has shown that these algorithms lead to models, trained on historical data, that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing and mitigating these biases throughout experimental pipe-lines is therefore a primary step for devising search and rec-ommendation algorithms that can be responsibly deployed in real-world applications.