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Transparent data mining for big and small data [electronic resource]

자료유형

E-Book(소장)

개인저자

Cerquitelli, Tania. Quercia, Daniele. Pasquale, Frank.

서명 / 저자사항

Transparent data mining for big and small data [electronic resource] / Tania Cerquitelli, Daniele Quercia, Frank Pasquale, editors.

발행사항

Cham : Springer, c2017.

형태사항

1 online resource (xv, 215 p.) : col. ill.

총서사항

Studies in Big Data,2197-6503, 2197-6511 (electronic) ; 32

ISBN

9783319540238 9783319540245 (e-book)

요약

This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches. As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.

일반주기

Title from e-Book title page.

내용주기

Part I: Transparent Mining -- Chapter 1: The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good -- Chapter 2: Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens -- Chapter 3: The Princeton Web Transparency and Accountability Project -- Part II: Algorithmic solutions -- Chapter 4: Algorithmic Transparency via Quantitative Input Influence -- Chapter 5 -- Learning Interpretable Classification Rules with Boolean Compressed Sensing -- Chapter 6: Visualizations of Deep Neural Networks in Computer Vision: A Survey -- Part III: Regulatory solutions -- Chapter 7: Beyond the EULA: Improving Consent for Data Mining -- Chapter 8: Regulating Algorithms Regulation? First Ethico-legal Principles, Problems and Opportunities of Algorithms -- Chapter 9: Algorithm Watch: What Role Can a Watchdog Organization Play in Ensuring Algorithmic Accountability?

서지주기

Includes bibliographical references.

이용가능한 다른형태자료

Issued also as a book.

일반주제명

Data mining. Mass media --Law and legislation. Computer software. Engineering. Computer simulation. Big data.

바로가기

URL

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020		▼a 9783319540238
020		▼a 9783319540245 (e-book)
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084		▼a 006.312 ▼2 DDCK
090		▼a 006.312
245	0 0	▼a Transparent data mining for big and small data ▼h [electronic resource] / ▼c Tania Cerquitelli, Daniele Quercia, Frank Pasquale, editors.
260		▼a Cham : ▼b Springer, ▼c c2017.
300		▼a 1 online resource (xv, 215 p.) : ▼b col. ill.
490	1	▼a Studies in Big Data, ▼x 2197-6503, ▼x 2197-6511 (electronic) ; ▼v 32
500		▼a Title from e-Book title page.
504		▼a Includes bibliographical references.
505	0	▼a Part I: Transparent Mining -- Chapter 1: The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good -- Chapter 2: Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens -- Chapter 3: The Princeton Web Transparency and Accountability Project -- Part II: Algorithmic solutions -- Chapter 4: Algorithmic Transparency via Quantitative Input Influence -- Chapter 5 -- Learning Interpretable Classification Rules with Boolean Compressed Sensing -- Chapter 6: Visualizations of Deep Neural Networks in Computer Vision: A Survey -- Part III: Regulatory solutions -- Chapter 7: Beyond the EULA: Improving Consent for Data Mining -- Chapter 8: Regulating Algorithms Regulation? First Ethico-legal Principles, Problems and Opportunities of Algorithms -- Chapter 9: Algorithm Watch: What Role Can a Watchdog Organization Play in Ensuring Algorithmic Accountability?
520		▼a This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches. As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.
530		▼a Issued also as a book.
538		▼a Mode of access: World Wide Web.
650	0	▼a Data mining.
650	0	▼a Mass media ▼x Law and legislation.
650	0	▼a Computer software.
650	0	▼a Engineering.
650	0	▼a Computer simulation.
650	0	▼a Big data.
700	1	▼a Cerquitelli, Tania.
700	1	▼a Quercia, Daniele.
700	1	▼a Pasquale, Frank.
830	0	▼a Studies in Big Data ; ▼v 32.
856	4 0	▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-54024-5
945		▼a KLPA
991		▼a E-Book(소장)

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No.	소장처	청구기호	등록번호	도서상태	반납예정일	예약	서비스
No. 1	소장처 중앙도서관/e-Book 컬렉션/	청구기호 CR 006.312	등록번호 E14019082	도서상태 대출불가(열람가능)	반납예정일	예약	서비스 M

Transparent data mining for big and small data [electronic resource]

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