Practical Fairness: Achieving Fair and Secure Data Models

Nielsen, Aileen

  • 出版商: O'Reilly
  • 出版日期: 2021-01-05
  • 定價: $1,890
  • 售價: 8.0$1,512
  • 語言: 英文
  • 頁數: 326
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1492075736
  • ISBN-13: 9781492075738
  • 相關分類: 人工智慧Data Science資訊安全
  • 立即出貨 (庫存=1)

商品描述

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 practices
  • Understand complex interrelationships between fairness, privacy, and data security
  • Use preventive measures to minimize bias when developing data modeling pipelines
  • Identify opportunities for bias and discrimination in current data scientist models
  • Detect data pipeline aspects that implicate security and privacy concerns

商品描述(中文翻譯)

公平性對於數據科學家來說越來越重要。越來越多的證據表明,在商業和政府中廣泛應用機器學習和人工智能正在重現我們一直在現實世界中努力對抗的偏見。但是,當涉及到代碼時,公平性意味著什麼呢?這本實用書籍涵蓋了與數據安全和隱私相關的基本問題,以幫助人工智能和數據專業人員使用公平且沒有偏見的代碼。

如今,在數據管道的每個步驟中都出現了許多現實的最佳實踐,從數據選擇和預處理到黑盒模型審計。作者Aileen Nielsen將引導您了解使代碼公平且安全的技術、法律和道德方面,同時強調與公平性和算法相關的最新學術研究和持續的法律發展。

- 編寫遵循公平機器學習最佳實踐的數據處理和建模代碼
- 理解公平性、隱私和數據安全之間的複雜相互關係
- 在開發數據建模管道時使用預防措施來減少偏見
- 確定當前數據科學家模型中的偏見和歧視機會
- 檢測涉及安全和隱私問題的數據管道方面

作者簡介

Aileen Nielsen is a software engineer who has analyzed data in a variety of settings from a physics laboratory to a political campaign to a healthcare startup. She also has a law degree and splits her time between a deep learning startup and research as a Fellow in Law and Technology at ETH Zurich. She has given talks around the world on fairness issues in data and modeling.

作者簡介(中文翻譯)

Aileen Nielsen是一位軟體工程師,她在各種場景中分析數據,包括物理實驗室、政治競選和醫療初創公司。她還擁有法學學位,並將時間分配在一家深度學習初創公司和ETH Zurich的法律與技術研究員職位上。她在世界各地就數據和建模中的公平問題發表過演講。