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...
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| Other Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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| Series: | Studies in Big Data,
32 |
| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- 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?
