This book is a collection of reflections by thought leaders at first-mover organizations in the exploding field of "Data Science for Social Good", meant as the application of knowledge from computer science, complex systems and computational social science to challenges such as humanitarian response, public health, sustainable development. The book provides both an overview of scientific approaches to social impact – identifying a social need, targeting an intervention, measuring impact – and the complementary perspective of funders and philanthropies that are pushing forward this new sector. This book will appeal to students and researchers in the rapidly growing field of data science for social impact, to data scientists at companies whose data could be used to generate more public value, and to decision makers at nonprofits, foundations, and agencies that are designing their own agenda around data.

Using biographies of three natural scientists--geologist Clarence King, forester Robert Marshall, and biologist Rachel Carson--Science and the Social Good investigates the links between nature's scientific study and social improvement.

A landmark tour of the new science of "idea flow" outlines revolutionary insights into the mysteries of collective intelligence and social influence, explaining the virtually unlimited data sets of today's digital technologies and the considerable accuracy of information from social networks.

With the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral reefs, rivers, and other natural resources. It presents how the joint efforts of researchers in computer science, ecology, economics, and psychology help address the goals of the United Nations' 2030 Agenda for Sustainable Development. Written at a level accessible to conservation professionals and AI researchers, the book offers both an overview of the field and an in-depth view of how AI is being used to understand patterns in wildlife poaching and enhance patrol efforts in response, covering research advances, field tests and real-world deployments. The book also features efforts in other major conservation directions, including protecting natural resources, ecosystem monitoring, and bio-invasion management through the use of game theory, machine learning, and optimization.

An introductory guide with real-life examples on using AI to help homeless youth, diabetes patients, and other social welfare interventions.

How the NSF became an important yet controversial patron for the social sciences, influencing debates over their scientific status and social relevance. In the early Cold War years, the U.S. government established the National Science Foundation (NSF), a civilian agency that soon became widely known for its dedication to supporting first-rate science. The agency's 1950 enabling legislation made no mention of the social sciences, although it included a vague reference to "other sciences." Nevertheless, as Mark Solovey shows in this book, the NSF also soon became a major--albeit controversial--source of public funding for them.

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019. The chapter "Supervised Human-guided Data Exploration" is published open access under a Creative Commons Attribution 4.0 International license (CC BY).

Since the eighteenth century when natural historians created the idea of distinct racial categories, scientific findings on race have been a double-edged sword. For some antiracists, science holds the promise of one day providing indisputable evidence to help eradicate racism. On the other hand, science has been enlisted to promote racist beliefs ranging from a justification of slavery in the eighteenth century to the infamous twentieth-century book, The Bell Curve, whose authors argued that racial differences in intelligence resulted in lower test scores for African Americans. This well-organized, readable textbook takes the reader through a chronological account of how and why racial categories were created and how the study of "race" evolved in multiple academic disciplines, including genetics, psychology, sociology, and anthropology. In a bibliographic essay at the conclusion of each of the book's seven sections, the authors recommend primary texts that will further the reader's understanding of each topic. Heavily illustrated and enlivened with sidebar biographies, this text is ideal for classroom use.

This volume showcases the presentations and discussions delivered at the 2018 POMS International Conference in Rio. Through a collection of selected papers, it is possible to review the impact and application of operations management for social good, with contributions across a wide range of topics, including: humanitarian operations and crisis management, healthcare operations management, sustainable operations, artificial intelligence and data analytics in operations, product innovation and technology in operations management, marketing and operations management, service operations and servitization, logistics and supply chain management, resilience and risk in operations, defense, and tourism among other emerging Operations Management issues. The Production and Operations Management Society (POMS) is one of the most important and influential societies in the subject of Production Engineering and, as an international professional and academic organization, represents the interests of professionals and academics in production management and operations around the world.