Seeking to increasing the social awareness of citizens, institutions, and corporations with regard to the risks presented by the acritical use of algorithms in decision-making, this book explains the rationale and the methods of algorithm audit. Interdisciplinary in approach, it provides a systematic overview of the subject, supplying readers with clear definitions and practical tools for the audit of algorithms, while also taking account of the political, business, and vocational obstacles to the development of this new field. As such, it constitutes an essential resource for students and researchers across the social sciences and humanities, as well as for professionals and policymakers, with concerns about the social consequences of algorithmic decision-making.

This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.