Creating search and recommendation algorithms that are efficient and effective has been the main objective for the industry 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 pipelines is therefore a primary step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. In this workshop, we aim to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. More info at workshop website