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Titlebook: Living with Monsters? Social Implications of Algorithmic Phenomena, Hybrid Agency, and the Performat; IFIP WG 8.2 Working Ulrike Schultze,

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书目名称Living with Monsters? Social Implications of Algorithmic Phenomena, Hybrid Agency, and the Performat
副标题IFIP WG 8.2 Working
编辑Ulrike Schultze,Margunn Aanestad,Kai Riemer
视频video
丛书名称IFIP Advances in Information and Communication Technology
图书封面Titlebook: Living with Monsters? Social Implications of Algorithmic Phenomena, Hybrid Agency, and the Performat; IFIP WG 8.2 Working  Ulrike Schultze,
描述This book constitutes the refereed proceedings of the IFIP WG 8.2 Working Conference on Information Systems and Organizations, IS&O 2018, held in San Francisco, CA, USA, in December 2018..The 11 revised full papers presented together with one short paper and 2 keynote papers were carefully reviewed and selected from 47 submissions. The papers are organized in the following topical sections: setting the stage; social implications of algorithmic phenomena; hybrid agency and the performativity of technology; and living with monsters.
出版日期Conference proceedings 2018
关键词big data; big datum; cloud computing; computer science; databases; digital libraries; digital objects; educ
版次1
doihttps://doi.org/10.1007/978-3-030-04091-8
isbn_ebook978-3-030-04091-8Series ISSN 1868-4238 Series E-ISSN 1868-422X
issn_series 1868-4238
copyrightIFIP International Federation for Information Processing 2018
The information of publication is updating

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Quantifying Quality: Towards a Post-humanist Perspective on Sensemakinge presupposes the quantification of qualitative phenomena. We analyse an ongoing case where the core of the qualitative – judgements, assessments, sensemaking – is being challenged by quantification through Big Data/data science-inspired new digital tools. Concretely, we study how traditionally qual
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Understanding the Impact of Transparency on Algorithmic Decision Making Legitimacylso generated several challenges not the least of which is humanities ability to analyze, process and take decisions on that data. Algorithmic Decision Making (ADM) represents a solution to these challenge. Whilst ADM has been around for many years, it has come under increased scrutiny in recent yea
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Hotspots and Blind Spotsby the emergence of the new occupational role of “intelligence officer”. While intelligence officers were initially intended to merely support police officers by making sense of algorithmic outputs, they became increasingly influential in steering police action based on their judgments. Paradoxicall
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