Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world's best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy.
- The future of the data republic, life within a data democracy, and our digital freedoms
- An in-depth analysis of open science, open data, open source software, and their future challenges
- A comprehensive review of data democracy's implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology
- The democratization of Artificial Intelligence (AI), and data issues such as: Bias, imbalance, context, and knowledge extraction
- A systematic review of AI methods applied to software engineering problems
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Section I The data republic 1. Data democracy for you and me (bias, truth, and context) 2. Data citizens: rights and responsibilities in a data republic 3. The history and future prospects of open data and open source software 4. Mind mapping in arti?cial intelligence for data democracy 5. Foundations of data imbalance and solutions for a data democracy
Section II Implications of a data democracy 6. Data openness and democratization in healthcare: an evaluation of hospital ranking methods 7. Knowledge formulation in the health domain: a semiotics-powered approach to data analytics and democratization 8. Landsat's past paves the way for data democratization in earth science 9. Data democracy for psychology: how do people use contextual data to solve problems and why is that important for AI systems? 10. The application of arti?cial intelligence in software engineering: a review challenging conventional wisdom
Feras A. Batarseh is a Teaching Assistant Professor with the Data Analytics Program at Georgetown University, Washington, D.C., and a Research Assistant Professor with the College of Science at George Mason University (GMU), Fairfax, VA. His research and teaching span the areas of Data Science, Artificial Intelligence, and Context-Aware Software Systems. Dr. Batarseh obtained his PhD and MSc in Computer Engineering from the University of Central Florida (UCF) (2007, 2011) and a Graduate Certificate in Project Leadership from Cornell University (2016). His research work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published and edited several book chapters. He is the author and editor of Federal Data Science , another book by Elsevier's Academic Press. Dr. Batarseh has taught data science and software engineering courses at multiple universities including Georgetown, GMU, UCF, The University of Maryland, Baltimore County (UMBC), as well as George Washington University (GWU).
Ruixin Yang is an Associate Professor in the Department of Geography and GeoInformation Sciences (GGS) - College of Science at George Mason University (GMU), Fairfax, VA. He received his PhD in Aerospace Engineering from University of Southern California (USC) in 1990. His research work ranged from Fluid Dynamics to Astrophysics and General Relativity to Data Science, Information Systems, Data Mining, and Earth Systems Science. Dr. Yang led a software development team that built several prototypes for earth science information systems. His recent research is focused on data mining methods for hurricane-related earth science. He has published several referred papers on earth science data search, online analysis, metadata management, content-based search, and big data analytics.