TY  - BOOK
AU  - Cerquitelli,Tania
AU  - Quercia,Daniele
AU  - Pasquale,Frank
ED  - SpringerLink (Online service)
TI  - Transparent Data Mining for Big and Small Data
T2  - Studies in Big Data,
SN  - 9783319540245
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2017///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Data mining
KW  - Mass media
KW  - Law
KW  - Algorithms
KW  - Computational complexity
KW  - Computer simulation
KW  - Big data
KW  - Data Mining and Knowledge Discovery
KW  - IT Law, Media Law, Intellectual Property
KW  - Algorithm Analysis and Problem Complexity
KW  - Complexity
KW  - Simulation and Modeling
KW  - Big Data/Analytics
N1  - 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?; Available to subscribing member institutions only. Доступно лише організаціям членам підписки
N2  - 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
UR  - https://doi.org/10.1007/978-3-319-54024-5
ER  -