{"product_id":"responsible-data-science-paperback","title":"Responsible Data Science - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eGrant Fleming\u003c\/b\u003e (Author), \u003cb\u003ePeter C. Bruce\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eExplore the most serious prevalent ethical issues in data science with this insightful new resource\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of \"Black box\" algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eResponsible Data Science\u003c\/i\u003e delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eImprove model transparency, even for black box models\u003c\/li\u003e \u003cli\u003eDiagnose bias and unfairness within models using multiple metrics\u003c\/li\u003e \u003cli\u003eAudit projects to ensure fairness and minimize the possibility of unintended harm\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for data science practitioners, \u003ci\u003eResponsible Data Science\u003c\/i\u003e will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eA PRACTICAL GUIDE TO IDENTIFYING AND REDUCING BIAS AND UNFAIRNESS IN DATA SCIENCE\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eRapid advancements in data science are causing increasing alarm around the world as governments, companies, other organizations, and individuals put new technologies to uses that were unimaginable just a decade ago. Medicine, finance, criminal justice, law enforcement, communication, marketing and other functions are all being transformed by the implementation of techniques and methods made possible by progressively more obscure manipulations of larger and larger data sets. Almost every day, new stories of AI gone awry appear. What can be done to avoid these issues?\u003c\/p\u003e\u003cp\u003e\u003ci\u003eResponsible Data Science\u003c\/i\u003e is an insightful and practical exploration of the ethical issues that arise when the newest AI technologies are applied to the largest and most sensitive data sets on the planet. The book walks you through how to implement and audit cutting-edge AI models in ways that minimize the risks of unanticipated harms. It combines detailed technical analysis with perceptive social observations to offer data scientists a real-world perspective on their field.\u003c\/p\u003e\u003cp\u003eThe inability to explain how an artificial intelligence model uses inputs can jeopardize the willingness of regulators to even consider whether these technologies comply with existing and future regulatory and legal requirements. In this book you'll learn how to improve the interpretability of AI models, and audit them to reduce bias and unfairness, thereby inspiring greater confidence in the minds of customers, employees, regulators, legislators and other stakeholders.\u003c\/p\u003e\u003cp\u003ePerfect for data science practitioners, statisticians, software engineers, and technically aware managers and solutions architects, \u003ci\u003eResponsible Data Science\u003c\/i\u003e will also earn a place in the libraries of regulators, lawyers, and policy makers whose decisions will determine how and when data solutions are implemented.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThis groundbreaking book also covers: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\u003cb\u003e\u003cli\u003eThe various types of ethical challenges confronting modern day data scientists\u003c\/li\u003e\n\u003cli\u003eHow the adoption of \"black box\" models can aggravate issues of model transparency, bias, and fairness\u003c\/li\u003e\n\u003cli\u003eHow moral concepts like fairness translate (or fail to translate) into a modeling context\u003c\/li\u003e\n\u003cli\u003eHow model-agnostic methods can be used to make models more interpretable, identify issues of bias, and mitigate the bias discovered\u003c\/li\u003e\u003c\/b\u003e\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGRANT FLEMING\u003c\/b\u003e is a Data Scientist at Elder Research Inc. His professional focus is on machine learning for social science applications, model interpretability, civic technology, and building software tools for reproducible data science.\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePETER BRUCE\u003c\/b\u003e is the Senior Learning Officer at Elder Research, Inc., author of several best-selling texts on data science, and Founder of the Institute for Statistics Education at Statistics.com, an Elder Research Company.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 304\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.6 x 9.1 x 7.3 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 11, 2021\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47212866568441,"sku":"9781119741756","price":40.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/Sy9kUWVzdmR0blJwT0FQUkxqa0tyUT09.webp?v=1768098754","url":"https:\/\/bookscloud.io\/products\/responsible-data-science-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}