Transparent Data Mining for Big and Small Data
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 soluti...
Συγγραφή απο Οργανισμό/Αρχή: | |
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Άλλοι συγγραφείς: | , , |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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Σειρά: | Studies in Big Data,
32 |
Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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?