Responsible Data Science Av Grant (Massachusetts Institute Of Technology) Fleming, Peter C. (Massachusetts Institute Of Technology) Bruce

<p><b>Explore the most serious prevalent ethical issues in data science with this insightful new resource</b></p><p>The 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.</p><p><i>Responsible Data Science</i> delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical man