9781492075738 - practical fairness: achieving fair and secure data models di nielsen, aileen (21 risultati)
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paperback. Condizione: Good. Good reading copy of a PLEASE NOTE: Ex-Library edition with the usual stamps and stickers. Officially withdrawn from the library and stamped "no longer property of library," purchased at a charity even for the library system Otherwise, a clean text -- NO writing, NO highlighting to text. A useful rea…ding copy. Oversized. Clean text -- NO writing, NO highlighting to text.ÂPLEASE NOTE: Domestic US media (standard) US orders ONLY. NO international orders.
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Condizione: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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Paperback or Softback. Condizione: New. Practical Fairness: Achieving Fair and Secure Data Models. Book.
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Paperback. Condizione: New. Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it comes to code…? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.Identify potential bias and discrimination in data science modelsUse preventive measures to minimize bias when developing data modeling pipelinesUnderstand what data pipeline components implicate security and privacy concernsWrite data processing and modeling code that implements best practices for fairnessRecognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning modelsApply normative and legal concepts relevant to evaluating the fairness of machine learning models.
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Paperback. Condizione: new. Paperback. Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we've been trying to fight in the real world. But what does fairness mean when…it comes to code? This practical book covers basic concerns related to data security and privacy to help AI and data professionals use code that's fair and free of bias. Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to black box model audits. Author Aileen Nielsen guides you through the technical, legal, and ethical aspects of making code fair and secure while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms. Write data processing and modeling code that follows fair machine learning best practicesUnderstand complex interrelationships between fairness, privacy, and data securityUse preventive measures to minimize bias when developing data modeling pipelinesIdentify opportunities for bias and discrimination in current data scientist modelsDetect data pipeline aspects that implicate security and privacy concerns Fairness is becoming a paramount consideration for data scientists. This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Paperback. Condizione: Brand New. 330 pages. 9.50x7.25x0.75 inches. In Stock.
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Condizione: New. In English.
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Condizione: New. 2020. Paperback. . . . . .
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Paperback. Condizione: New. Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it comes to code…? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.Identify potential bias and discrimination in data science modelsUse preventive measures to minimize bias when developing data modeling pipelinesUnderstand what data pipeline components implicate security and privacy concernsWrite data processing and modeling code that implements best practices for fairnessRecognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning modelsApply normative and legal concepts relevant to evaluating the fairness of machine learning models.
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Paperback. Condizione: new. Paperback. Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we've been trying to fight in the real world. But what does fairness mean when…it comes to code? This practical book covers basic concerns related to data security and privacy to help AI and data professionals use code that's fair and free of bias. Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to black box model audits. Author Aileen Nielsen guides you through the technical, legal, and ethical aspects of making code fair and secure while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms. Write data processing and modeling code that follows fair machine learning best practicesUnderstand complex interrelationships between fairness, privacy, and data securityUse preventive measures to minimize bias when developing data modeling pipelinesIdentify opportunities for bias and discrimination in current data scientist modelsDetect data pipeline aspects that implicate security and privacy concerns Fairness is becoming a paramount consideration for data scientists. This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 58,70
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Taschenbuch. Condizione: Neu. Neuware - Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it c…omes to code This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.- Identify potential bias and discrimination in data science models- Use preventive measures to minimize bias when developing data modeling pipelines- Understand what data pipeline components implicate security and privacy concerns- Write data processing and modeling code that implements best practices for fairness- Recognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning models- Apply normative and legal concepts relevant to evaluating the fairness of machine learning models.
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Da: moluna, Greven, Germaniamoluna
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EUR 45,22
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Condizione: New. Fairness is becoming a paramount consideration for data scientists. This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that s fair and free of bias.Über den Autorrnr.
