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008 210316t20222022enka b 001 0 eng d
010 _a 2021935490
020 _a9781529732542
020 _a9781529732535 (paperback)
_q(paperback)
040 _aYDX
_beng
_erda
_cYDX
_dUKMGB
_dOCLCF
_dOCLCO
_dMYG
_dGRU
_dBDX
_dFIE
_dQGJ
_dOCLCO
_dNTU
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_dNLMAA
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_dWAU
_dDLC
_dBR-SpNIC
082 0 4 _a005.7
_223
100 1 _aBeaulieu, Anne,
_d1970-
245 1 0 _aData and society :
_ba critical introduction
264 1 _aLondon :
_bSage Publications Ltd,
_c[2022]
264 4 _c©2022
300 _a246 p.
_billustrations ;
_c25 cm
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
504 _aIncludes bibliographic references and index.
505 0 0 _aMachine generated contents note:
_gpt. I
_tData in Society --
_g1.
_tData in Society --
_g1.1.
_tIntroduction: Who cares about data? --
_g1.2.
_tDatafication and its components --
_g1.3.
_tData, ethics and knowledge production --
_g1.4.
_tConclusion: The impact of Datafication --
_gpt. II
_tData Creation --
_g2.
_tBig Data in Context --
_g2.1.
_tIntroduction: The rise of Big Data --
_g2.2.
_tThe Big Data mythology: Data transforms society --
_g2.3.
_tA historical perspective: Society transforms data --
_g2.4.
_tConclusion: Data do not speak for themselves --
_g3.
_tCharacteristics of Data --
_g3.1.
_tIntroduction: Data do not stay still --
_g3.2.
_tData are not neutral --
_g3.3.
_tData are context-dependent --
_g3.4.
_tConclusion: Characteristics of data --
_g4.
_tData, Evidence and Knowledge --
_g4.1.
_tIntroduction: The representational and the relational views on data --
_g4.2.
_tWhat is evidence? The path from data to knowledge --
_g4.3.
_tExamples of data within the knowledge production cycle --
_g4.4.
_tContrasting the representational and relational perspectives --
_g4.5.
_tConclusion: Data science in a relational perspective --
_gpt. III
_tData Circulation --
_g5.
_tPutting Data to Work --
_g5.1.
_tIntroduction: The complexity of putting data to work --
_g5.2.
_tThe challenge of `messy' data --
_g5.3.
_tInfrastructures --
_g5.4.
_tConventions and metadata --
_g5.5.
_tModels --
_g5.6.
_tVisualisations: Forms, tools and interfaces --
_g5.7.
_tCuration --
_g5.8.
_tConclusion: Forms of data work --
_g6.
_tNew Data Skills --
_g6.1.
_tIntroduction: Data expertise --
_g6.2.
_tWhat is data science? --
_g6.3.
_tData science skills --
_g6.4.
_tBringing skills together --
_g6.5.
_tConclusion: Becoming a data scientist today --
_g7.
_tGovernance of Data Journeys --
_g7.1.
_tIntroduction: What is data governance? --
_g7.2.
_tData as private commodities: Closed data --
_g7.3.
_tData as public goods: Open data --
_g7.4.
_tA hard case: The journeys of health-related data --
_g7.5.
_tShifting focus to usable data: The FAIR principles --
_g7.6.
_tInternational data journeys and the problem of data inequities --
_g7.7.
_tConclusion: Governance is not a silver bullet --
_gpt. IV
_tData Value, Innovation and Responsibility --
_g8.
_tData as a Source of Value --
_g8.1.
_tIntroduction: What makes data valuable? --
_g8.2.
_tAssumptions about the value of data --
_g8.3.
_tData and innovation --
_g8.4.
_tThe data economy --
_g8.5.
_tWho benefits from the value of data? --
_g8.6.
_tAllocating value, responsibility and profit --
_g8.7.
_tHow does AI add value to data? --
_g8.8.
_tThe value of prediction --
_g8.9.
_tThe value of metrics --
_g8.10.
_tConclusion: Making data valuable --
_g9.
_tData Justice and Ethics --
_g9.1.
_tIntroduction: From data value to data ethics --
_g9.2.
_tWhich data are ethically sensitive? --
_g9.3.
_tData justice: Implementing fairness --
_g9.4.
_tEthics for data work: General frameworks --
_g9.5.
_tEthics in data work: Assessing technical decisions --
_g9.6.
_tResponsibilities of data workers --
_g9.7.
_tConclusion: From analysis to action, from rules to power --
_g10.
_tResponsible Use of Data as Evidence --
_g10.1.
_tIntroduction: Data matters --
_g10.2.
_tWhat is evidence-based decision making? --
_g10.3.
_tEnsuring responsible use of data --
_g10.4.
_tLegal frameworks and formal regulation --
_g10.5.
_tCodes of conduct --
_g10.6.
_tComputational metrics and design --
_g10.7.
_tOrganisational and cultural interventions --
_g10.8.
_tInstitutional Review Boards --
_g10.9.
_tSocial participation and slow science --
_g10.10.
_tConclusion: Responsibility, monitoring and trust --
_gpt. V
_tConclusion: Data and the Knowledge We Need --
_g11.
_tTowards Good Data Science --
_g11.1.
_tLesson 1: `Data' is a relational category --
_g11.2.
_tLesson 2: Infrastructures and data stewardship are essential to extract knowledge from Big Data --
_g11.3.
_tLesson 3: Data workers must use data sources with discernment and be aware of the risks of discrimination and inequality connected to data --
_g11.4.
_tLesson 4: Ethics, security and social responsibility are a fundamental part of data work --
_g11.5.
_tLesson 5: Responsible data work requires social dialogue, community engagement and contributions to data literacy.
520 _aData and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the "Big Data" hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work. --
_cProvided by publisher.
650 0 _aBig data.
650 0 _aBig data
_xaspectos sociais
_9346
700 1 _aLeonelli, Sabina,
_eCoautor
942 _2ddc
_cL
_k005.7
_mB377b
999 _c2088
_d2088