{"product_id":"practical-fairness-achieving-fair-and-secure-data-models","title":"Practical Fairness: Achieving Fair and Secure Data Models","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThis book provides practical guidance for data and AI professionals to develop fair and secure code by covering basic concerns related to data security and privacy, highlighting up-to-date academic research, and discussing legal developments. \u003c\/blockquote\u003e\u003cp\u003e                                                            \u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e                              \u003cstrong\u003eLength\u003c\/strong\u003e: 175 pages\u003cbr\u003e                              \u003cstrong\u003ePublication date\u003c\/strong\u003e: 18 December 2020\u003cbr\u003e                              \u003cstrong\u003ePublisher\u003c\/strong\u003e: O'Reilly Media, Inc, USA\u003cbr\u003e                          \u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThe growing importance of fairness in data science is becoming increasingly evident. Mounting evidence suggests that the widespread adoption of machine learning and AI in business and government is perpetuating the same biases we aim to combat in the real world. However, when it comes to code, what exactly does fairness mean? This practical book addresses fundamental concerns related to data security and privacy, empowering data and AI professionals to utilize code that is fair, unbiased, and free from discrimination.\u003cbr\u003e\u003cbr\u003eNumerous realistic best practices are emerging at every stage of the data pipeline, from data selection and preprocessing to closed model audits. Author Aileen Nielsen leads you through the technical, legal, and ethical aspects of creating code that is fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.\u003cbr\u003e\u003cbr\u003eIdentify potential bias and discrimination in data science models\u003cbr\u003e\u003cbr\u003eImplement preventive measures to minimize bias during the development of data modeling pipelines\u003cbr\u003e\u003cbr\u003eUnderstand which data pipeline components implicate security and privacy concerns\u003cbr\u003e\u003cbr\u003eWrite data processing and modeling code that follows best practices for fairness\u003cbr\u003e\u003cbr\u003eRecognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning models\u003cbr\u003e\u003cbr\u003eApply normative and legal concepts relevant to evaluating the fairness of machine learning models\u003c\/p\u003e\u003cp\u003e                            \u003cstrong\u003eWeight\u003c\/strong\u003e: 614g                            \u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 178 x 233 x 23 (mm)                            \u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781492075738                                                      \u003c\/p\u003e","brand":"Aileen Nielsen","offers":[{"title":"Paperback \/ softback","offer_id":44100318331130,"sku":"9781492075738","price":32.12,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/9d8b54b156bf76159303f74e7d080692.jpg?v=1621023704","url":"https:\/\/shulphink.com\/products\/practical-fairness-achieving-fair-and-secure-data-models","provider":"Shulph Ink","version":"1.0","type":"link"}