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"Seeking to increasing the social awareness of citizens, institutions, and corporations with regard 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 policy makers, with concerns about the social consequences of algorithmic decision-making"--

- If one defines «data culture» as the connection between the moment statistical information is produced and the moment it is used at a theoretical and practical level, one can identify the concept's two main dimensions: the quantity of statistics used for social information and the totality of organizational, technological and methodological changes that characterize the production of statistical services over a certain period of time. Analyzing its history from the Seventies to today, it is possible to outline the changes that led to a «new data culture», which generates consequences both for producers and users of statistics: social researchers first and foremost, but the general public as well.

- This paper reveals a contradiction between the idea of a changing society and the results of research on social stratification in people's perception: a kind of immobility appears. The authors think that this could be the result of their construction. Moreover they believe that social stratification changes are visible when looking to the criteria that people use to order occupations and evaluate their desirability.Key words: Occupational categories, Social desirability, Social change, Social representations, Reputational scales, Occupational stratification

Open science is considered a new science paradigm to make research accessible, accountable, and effective. Open science is already changing the academic profession starting from micro-practices to professional relations with epistemic communities and stakeholders, with implications that we are not yet able to predict. The article delves first into literature and official documentation to unfold the discursive regimes which sustain the spread of open science. A specific focus is then devoted to the professional transition, highlighting the role of funding organizations in setting the new science environment and the subjective experience of academics. The article is completed by a case study in the field of Research Data Management where the misalignment among incumbent/changing processes can be more apparent. Finally, a research agenda that focuses on how academic micro-practices are affecting organizations and science structures is proposed. This article aims at beginning to plow the ground for new research directions to emerge.

Abstract
The use of big data represents a valuable way to inspire decision-making in a time of scarce resources. The technological revolution is in fact enabling governments to use a great variety of digital tools and data to manage all phases of the policy cycle process, becoming a core element for e-governance applications and techniques. However, research is seemingly not yet aligned yet with the hybrid environment that both public policies and politics are moving in, while the actors (old and new) and the decision-making processes themselves, in their searching for automation and objectivity, risk being overshadowed. Taking the case of Higher Education, this article proposes a research framework for big-data use to prompt the reflection on the power of "evidence" in decision making; to question and contextualize such evidences in a multimodal and integrated scenario, and to understand the challenges that data will pose to education both in terms of unforeseen and hidden effects.

One way to study the politics of big data is the inspection of their assemblages. By opening up the "black boxes" of data assemblages, it is possible to reconstruct the choices, compromises, conflicts and agree-ments that contributed to the construction of a given datum. Leaning on in-depth interviews and focus groups with experts and specialists who work within three European data centers, we unveil the interde-pendence between social and technical aspects and between a series of internal and external actors, which all contribute to the data assemblage. Results show that communities of experts, technologies, stakeholders and end-users are entwined components that interact amongst them in a contingent and complex web of negotiations and constraints and frame what is possible, desirable and expected by data.

Intro -- Preface -- Contents -- 1 Introduction -- References -- Epistemology -- 2 On Data, Big Data and Social Research. Is It a Real Revolution? -- Abstract -- 1 Data-Bases Are not a Repository -- 2 Some Consequences for Building a Data Science for Social Research -- References -- 3 New Data Science: The Sociological Point of View -- Abstract -- 1 The Reframing of Social Sciences -- 2 Data as Signification Acts -- 3 Data Culture, Data Assemblage and Data Science -- 4 Sense Making, Small Decisions and Social Actors -- 5 Final Remarks -- References -- 4 Data Revolutions in Sociology -- Abstract -- References -- 5 Blurry Boundaries: Internet, Big-New Data, and Mixed-Method Approach -- Abstract -- 1 Introduction -- 2 New Contexts and New Data -- 3 The Study of the Online Textual Data -- 3.1 What Is New in Approaches and Analytics? -- 3.2 Concerns and Boundaries -- 4 Open Questions -- References -- 6 Social Media and the Challenge of Big Data/Deep Data Approach -- Abstract -- 1 The Field of Observation: The Nature of the Data and the Perspective of Collection -- 2 Computational Social Sciences and Big Data: New Research Questions -- 3 Big Data: Critical Issues -- 3.1 The Contingency of the UGC: The Dimension of Time Is Relevant -- 3.2 The Architecture of the Platforms Counts -- 3.3 Making Sense of Network -- 3.4 Deep Data and the Context of the Facts -- 4 Conclusions -- References -- 7 Governing by Data: Some Considerations on the Role of Learning Analytics in Education -- Abstract -- 1 Introduction -- 2 The Transformative Power of Data -- 3 System of Measurement -- 3.1 Description -- 3.2 Prediction/Prescription -- 3.3 Accountability -- 4 Culture of Evidence -- 4.1 Short Circuits -- 4.2 Collateral Risks -- 4.3 Methods -- 5 System of Influence -- 5.1 Hybrid Actors -- 5.2 Software Space -- 5.3 Transactional Pedagogies