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\u003chtml\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 \u0026ldquo;Black box\u0026rdquo; 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\u003c/html\u003e |
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