000 03975nam a2200517 i 4500
001 9072233
003 IEEE
005 20220712204950.0
006 m o d
007 cr |n|||||||||
008 200505s2020 mau ob 001 eng d
020 _a9780262358521
_qelectronic bk.
020 _z0262358522
_qelectronic bk.
020 _z9780262044004
035 _a(CaBNVSL)mat09072233
035 _a(IDAMS)0b0000648c95d0fe
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aHQ1190
_b.K375 2020eb
082 0 4 _a305.42
_223
100 0 _aKanarinka,
_eauthor.
_925889
245 1 0 _aData feminism /
_cCatherine D'Ignazio and Lauren F. Klein.
264 1 _aCambridge, Massachusetts :
_bThe MIT Press,
_c[2020]
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2020]
300 _a1 PDF (328 pages).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aStrong ideas
505 0 _aIntroduction: Why data science needs feminism -- Examine power : the power chapter -- Challenge power : collect, analyze, imagine, teach -- Elevate emotion and embodiment : on rational, scientific, objective viewpoints from mythical, imaginary, impossible standpoints -- Rethink binaries and hierarchies : "What gets counted counts" -- Embrace pluralism : unicorns, janitors, ninjas, wizards and rock stars -- Consider context : the numbers don't speak for themselves -- Make labor visible : show your work -- Conclusion: Now let's multiply.
506 _aRestricted to subscribers or individual electronic text purchasers.
520 _aA new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom Data science for whom Data science with whose interests in mind The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics--one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever "speak for themselves." Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
650 0 _aFeminism.
_925890
650 0 _aFeminism and science.
_925891
650 0 _aBig data
_xSocial aspects.
_925892
650 0 _aQuantitative research
_xMethodology
_xSocial aspects.
_925893
650 0 _aPower (Social sciences)
_925894
655 4 _aElectronic books.
_93294
700 1 _aKlein, Lauren F.,
_eauthor.
_925895
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_925896
710 2 _aMIT Press,
_epublisher.
_925897
830 0 _a<strong> ideas series.
_925898
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=9072233
942 _cEBK
999 _c73639
_d73639