9781492075738 - practical fairness: achieving fair and secure data models von nielsen, aileen (8 Ergebnisse)

Zustand: 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.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 330 pages. 9.50x7.25x0.75 inches. In Stock.

Zustand: New. In English.

Zustand: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: 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 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 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.…

Zustand: 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.&Uumlber den Autorrnr.