可靠的数据科学 Responsible Data Science

基本信息

Format:Paperback / softback 304 pages

Publisher:John Wiley & Sons Inc

Imprint:John Wiley & Sons Inc

ISBN:9781119741756

Published:24 Jun 2021

Weight:510g

Dimensions:189 x 233 x 18 (mm)

信息仅供参考，具体请以到手的实物为准

书籍简介●

利用这一富有洞察力的新资源探索数据科学中严重的普遍道德问题

数据科学的日益普及导致了许多广为人知的偏见、不公正和歧视案例。 即使对于其开发人员来说，“黑匣子”算法的广泛部署也难以或无法理解和解释，这是这些意外危害的主要来源，使得操纵大型数据集的现代技术和方法看起来险恶，甚至危险。 当这些算法落入不法分子手中时，它们就可以压制政治异议和迫害少数群体。 为了防止这些危害，各地的数据科学家必须了解他们构建和部署的算法可能会如何损害某些群体或不公平。

负责任的数据科学提供了如何以公平和道德的方式实施数据科学解决方案的全面、实用的方法，大限度地减少对社会弱势成员造成不当伤害的风险。 数据科学从业者和分析团队的管理者都将学习如何：

●提高模型透明度，即使对于黑盒模型也是如此

●使用多个指标诊断模型内的偏见和不公平现象

●审核项目以确保公平并大程度地减少意外伤害的可能性

《负责任的数据科学》非常适合数据科学从业者，也将在技术经理、软件开发人员和统计学家的书架上赢得一席之地。

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 large data sets seem sinister, even dangerous. When put in the hands of someone, 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.

作者简介

GRANT FLEMING 是 Elder Research Inc. 的数据科学家。他的专业重点是社会科学应用的机器学习、模型可解释性、公民技术以及为可再现数据科学构建软件工具。

PETER BRUCE 是 Elder Research, Inc. 的高级学习官，多本数据科学畅销书的作者，也是 Elder Research Company 的统计教育研究所的创始人。

GRANT FLEMING is a Data Scientist at Elder Research Inc. His professional focus is on machine learning for social science applications, model interpretability, civic technology, and building software tools for reproducible data science.

PETER BRUCE is the Senior Learning Officer at Elder Research, Inc., author of several best-selling texts on data science, and Founder of the Institute for Statistics Education at Statistics.com, an Elder Research Company.

目录

部分目录，仅供参考

Introduction xix

Part I Motivation for Ethical Data Science and Background Knowledge 1

Chapter 1 Responsible Data Science 3

The Optum Disaster 4

Jekyll and Hyde 5

Eugenics 7

Galton, Pearson, and Fisher 7

Ties between Eugenics and Statistics 7

Ethical Problems in Data Science Today 9

Predictive Models 10

From Explaining to Predicting 10

Predictive Modeling 11

Setting the Stage for Ethical Issues to Arise 12

Classic Statistical Models 12

Black-Box Methods 14

Important Concepts in Predictive Modeling 19

Feature Selection 19

Model-Centric vs. Data-Centric Models 20

Holdout Sample and Cross-Validation 20

Overfitting 21

Unsupervised Learning 22

The Ethical Challenge of Black Boxes 23

Two Opposing Forces 24

Pressure for More Powerful AI 24

Public Resistance and Anxiety 24

Summary 25

Chapter 2 Background: Modeling and the Black-Box Algorithm 27

Assessing Model Performance 27

Predicting Class Membership 28