We are living in an historical age in which automated decision making are rapidly spreading in nowadays economies transforming several domains of our daily life. Over the last few decades, such tools have become progressively complex by exploiting an ever-growing amount of personal and proxy data through increasingly sophisticated massive profiling techniques. Especially, ranking systems are one of the most relevant technologies in the transformation we are witnessing and there is almost nothing today that is not expressed in a ranking form: people, products, jobs, opinions. Ranking is therefore one of the predominant forms by which both online and offline software systems present results in a wide variety of domains ranging from web search engines to recommendation systems. Despite they have been widely employed since decades in Information Retrieval domain, they have recently come back at the cutting edge thanks to the explosive growth of computational power and data availability. The main task of ranking systems is to find an allocation of elements to each of the n positions so that the total value obtained is maximized. The success of an element and its exposure are strongly dependent by its position; a number of recent researches have shown that ranking algorithms whose only task is to maximize utility do not necessarily lead to fair or desirable scenarios, causing forms of algorithmic biases that in some cases can lead to serious social implications. Web search engine results that inadvertently promote stereotypes through over-representation of sensitive attributes such as gender, ethnicity and age are an example. For this reason, the study of fairness and bias in ranking algorithms is a fundamental task in this transformation era.