Grant Fleming – författare

Never before had a book been published which provides such a comprehensive study of Australian corporate leadership over the past 100 years. Written by a team of economic historians The Big End of Town, first published in 2004, is a proper business history of twentieth-century Australia. This book traces the evolution of large business enterprises in Australia, from the giants of the nineteenth century - such as Dalgety's, CSR and BHP - to the contemporary leaders in Newscorp and Qantas. It delves into why the market leaders became the major players, examines what was crucial to their success, and their roles in leading the Australian economy. By investigating their evolution this book provides a useful evaluation of the factors that have led to their competitive success and provides an essential guide for all businesses in Australia and beyond.

Never before had a book been published which provides such a comprehensive study of Australian corporate leadership over the past 100 years. Written by a team of economic historians The Big End of Town, first published in 2004, is a proper business history of twentieth-century Australia. This book traces the evolution of large business enterprises in Australia, from the giants of the nineteenth century - such as Dalgety's, CSR and BHP - to the contemporary leaders in Newscorp and Qantas. It delves into why the market leaders became the major players, examines what was crucial to their success, and their roles in leading the Australian economy. By investigating their evolution this book provides a useful evaluation of the factors that have led to their competitive success and provides an essential guide for all businesses in Australia and beyond.

Explore the most serious prevalent ethical issues in data science with this insightful new resource

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.

Responsible Data Science 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:

Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm

Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.

Explore the most serious prevalent ethical issues in data science with this insightful new resourceThe 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.Responsible Data Science 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: Improve model transparency, even for black box modelsDiagnose bias and unfairness within models using multiple metricsAudit projects to ensure fairness and minimize the possibility of unintended harmPerfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.

Explore the most serious prevalent ethical issues in data science with this insightful new resource

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.

Responsible Data Science 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:

Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm

Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.