Grant Fleming Peter C Bruce (12 results)
- Softcover
Seller: HPB-Red, Dallas, TX, U.S.A.HPB-Red
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£ 7.27
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Paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
- Softcover
Seller: Lakeside Books, Benton Harbor, MI, U.S.A.Lakeside Books
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£ 20.94
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Condition: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.
- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
Contact seller5-star sellerCondition: New
£ 27.00
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Paperback. Condition: New. 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 modelsDiagnose bias and unfairness within models using multiple metricsAudit 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.
- Softcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
Contact seller5-star sellerCondition: New
£ 28.16
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Paperback. Condition: New. 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 modelsDiagnose bias and unfairness within models using multiple metricsAudit 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.
- Softcover
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 29.69
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Condition: New.
- Softcover
Seller: Revaluation Books, Exeter, , United KingdomRevaluation Books
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£ 29.26
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Paperback. Condition: Brand New. 304 pages. 9.00x7.25x0.75 inches. In Stock.
- Softcover
Seller: Chiron Media, Wallingford, , United KingdomChiron Media
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£ 27.82
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Paperback. Condition: New.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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£ 42.35
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Condition: New.
- Softcover
Seller: Ubiquity Trade, Miami, FL, U.S.A.Ubiquity Trade
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£ 52.01
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Condition: New. Brand new! Please provide a physical shipping address.
- Softcover
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
Contact seller5-star sellerCondition: New
£ 28.87
£ 37.24 shippingShips within U.S.A.Quantity: 8 available
Paperback. Condition: New. 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 modelsDiagnose bias and unfairness within models using multiple metricsAudit 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.
- Softcover
Seller: moluna, Greven, , Germanymoluna
Contact seller5-star sellerCondition: New
£ 30.64
£ 42.37 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. GRANT FLEMING 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.PETER BRUCE is the Se.
- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
Contact seller5-star sellerCondition: New
£ 23.81
£ 65.00 shippingShips from United Kingdom to U.S.A.Quantity: 2 available
Paperback. Condition: New. 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 modelsDiagnose bias and unfairness within models using multiple metricsAudit 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.
