Responsible Data Science

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.

Autorentext

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.

Klappentext

A PRACTICAL GUIDE TO IDENTIFYING AND REDUCING BIAS AND UNFAIRNESS IN DATA SCIENCE

Rapid advancements in data science are causing increasing alarm around the world as governments, companies, other organizations, and individuals put new technologies to uses that were unimaginable just a decade ago. Medicine, finance, criminal justice, law enforcement, communication, marketing and other functions are all being transformed by the implementation of techniques and methods made possible by progressively more obscure manipulations of larger and larger data sets. Almost every day, new stories of AI gone awry appear. What can be done to avoid these issues?

Responsible Data Science is an insightful and practical exploration of the ethical issues that arise when the newest AI technologies are applied to the largest and most sensitive data sets on the planet. The book walks you through how to implement and audit cutting-edge AI models in ways that minimize the risks of unanticipated harms. It combines detailed technical analysis with perceptive social observations to offer data scientists a real-world perspective on their field.

The inability to explain how an artificial intelligence model uses inputs can jeopardize the willingness of regulators to even consider whether these technologies comply with existing and future regulatory and legal requirements. In this book you'll learn how to improve the interpretability of AI models, and audit them to reduce bias and unfairness, thereby inspiring greater confidence in the minds of customers, employees, regulators, legislators and other stakeholders.

Perfect for data science practitioners, statisticians, software engineers, and technically aware managers and solutions architects, Responsible Data Science will also earn a place in the libraries of regulators, lawyers, and policy makers whose decisions will determine how and when data solutions are implemented.

This groundbreaking book also covers:

• The various types of ethical challenges confronting modern day data scientists
• How the adoption of black box models can aggravate issues of model transparency, bias, and fairness
• How moral concepts like fairness translate (or fail to translate) into a modeling context
• How model-agnostic methods can be used to make models more interpretable, identify issues of bias, and mitigate the bias discovered

Inhalt

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

The Rare Class Problem 28

Lift and Gains 28

Area Under the Curve 29

AUC vs. Lift (Gains) 31

Predicting Numeric Values 32

Goodness-of-Fit 32

Holdout Sets and Cross-Validation 33

Optimization and Loss Functions 34

Intrinsically Interpretable Models vs. Black-Box Models 35

Ethical Challenges with Interpretable Models 38

Black-Box Models 39

Ensembles 39

Nearest Neighbors 41

Clustering 41

Association Rules 42

Collaborative Filters 42

Artificial Neural Nets and Deep Neural Nets 43

Problems with Black-Box Predictive Models 45

Problems with Unsupervised Algorithms 47

Summary 48

Chapter 3 The Ways AI Goes Wrong, and the Legal Implications 49

AI and Intentional Consequences by Design 50

Deepfakes 50

Supporting State Surveillance and Suppression 51

Behavioral Manipulation 52

Automated Testing to Fine-Tune Targeting 53

AI and Unintended Consequences 55

Healthcare 56

Finance 57

Law Enforcement 58

Technology 60

The Legal and Regulatory Landscape around AI 61

Ignorance Is No Defense: AI in the Context of Existing Law and Policy 63

A Finger in the Dam: Data Rights, Data Privacy, and Consumer Protection Regulations 64

Trends in Emerging Law and Policy Related to AI 66

Summary 69

Part II The Ethical Data Science Process 71

Chapter 4 The Responsible Data Science Framework 73

Why We Keep Building Harmful AI 74

Misguided Need for Cutting-Edge Models 74

Excessive Focus on Predictive Performance 74

Ease of Access and the Curse of Simplicity 76

The Common Cause 76

The Face Thieves 78

An Anatomy of Modeling Harms 79

The World: Context Matters for Modeling 80

The Data: Representation Is Everything 83

The Model: Garbage In, Danger Out 85

Model Interpretability: Human Understanding for Superhuman Models 86

Efforts Toward a More Responsible Data Science 89

Principles Are the Focus 90

Nonmaleficence 90

Fairness 90

Transparency 91

Accountability 91

Privacy 92

Bridging the Gap Between Principles and Practice with the Responsible Data Science (RDS) Framework 92

Justification 94

Compilation 94

Preparation 95

Modeling 96

Auditing 96

Summary 97

Chapter 5 Model Interpretability: The What and the Why 99

The Sexist Résumé Screener 99 The Necessity of Model In...