Responsible Data Science

Bruce, Peter C., Fleming, Grant

Responsible Data Science
  • 出版商: Wiley
  • 出版日期: 2021-05-11
  • 售價: $1,360
  • 貴賓價: 9.5 折 $1,292
  • 語言: 英文
  • 頁數: 304
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1119741750
  • ISBN-13: 9781119741756
  • 相關分類: Data Science

    • 立即出貨 (庫存 < 4)

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商品描述

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 models Diagnose bias and unfairness within models using multiple metrics Audit 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.

商品描述(中文翻譯)

探索數據科學中最嚴重且普遍的道德問題，這本富有洞察力的新資源將助你一臂之力。

數據科學的日益普及導致了許多廣為人知的偏見、不公正和歧視案例。廣泛使用的「黑盒子」算法往往難以理解和解釋，即使對於開發者來說也是如此，這是這些意外傷害的主要來源，使得對大數據集進行操作的現代技術和方法看起來陰險甚至危險。當這些算法落入專制政府之手時，它們使政治異議被壓制，少數族群受到迫害。為了防止這些傷害，全球的數據科學家必須了解他們所建立和部署的算法可能對某些群體造成傷害或不公平。

《負責任的數據科學》提供了全面而實用的方法，以公正和道德的方式實施數據科學解決方案，最大程度地減少對弱勢社會成員的不當傷害風險。數據科學從業者和分析團隊的管理者將學習如何：

- 提高模型的透明度，即使是黑盒子模型
- 使用多種指標診斷模型中的偏見和不公平
- 審計項目以確保公平，並最大程度地減少意外傷害的可能性

非常適合數據科學從業者閱讀，《負責任的數據科學》也將成為技術傾向的管理者、軟體開發人員和統計學家的書架上的必備之作。

作者簡介

Peter Bruce founded the Institute for Statistics Education at Statistics.com in 2002. The Institute specializes in introductory and graduate level online education in statistics, optimization, risk modeling, predictive modeling, data mining, and other subjects in quantitative analytics.

Grant Fleming is a Data Scientist at Elder Research Inc. (ERI). During his time at ERI, he has worked with clients in both government and the private sector on statistical testing, data asset creation, predictive analytics, and latent variable modeling. He has given multiple talks on machine learning interpretability and fairness within ERI as well as to outside groups. Internally to ERI, Grant is working on developing software packages for creating reproducible and interpretable black box models.

作者簡介(中文翻譯)

Peter Bruce於2002年在Statistics.com創立了統計教育研究所(Institute for Statistics Education)。該研究所專注於統計學、優化、風險建模、預測建模、數據挖掘和其他量化分析領域的入門和研究生級別的在線教育。

Grant Fleming是Elder Research Inc. (ERI)的數據科學家。在ERI的時間裡，他與政府和私營部門的客戶合作進行統計測試、數據資產創建、預測分析和潛變量建模。他曾就機器學習的可解釋性和公平性在ERI內部以及外部團體進行多次演講。在ERI內部，Grant正在開發用於創建可重現和可解釋的黑盒模型的軟件包。

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