Grant Fleming, Peter C. Bruce

Responsible Data Science (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 304
• Erscheinungstermin: 21. April 2021
• Englisch
• ISBN-13: 9781119741640
• Artikelnr.: 61626655

Autorenporträt

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.

Inhaltsangabe

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

Justific