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.
Formatted Contents Note
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?
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