Responsible Data Science

Grant Fleming, Peter C. Bruce

• Publisher: Wiley
• Year: 2021
• Print ISBN: 9781119741756
• eBook ISBN: 9781119741640
• Edition: 1
• Topic: Ciencia de la computación
• Subtopic: Minería de datos

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.

Part I
Motivation for Responsible Data Science and Background Knowledge

In This Part

Chapter 1: Responsible Data Science

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

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

CHAPTER 1
Responsible Data Science

Data science is an interdisciplinary field that combines elements of statistics, computer science, and information technology to generate useful insights from the increasingly large datasets that are generated in the normal course of business. Data science helps organizations capture value from their data, reducing costs and increasing profits, and also enables completely new types of endeavors, such as powerful information search and self-driving cars. Sometimes, data science projects can go awry, when the predictions made by statistical and machine learning algorithms turn to be not just wrong, but biased and unfair in ways that cause harm. History has shown that the dual good and evil nature of statistical methods is not new, but rather a characteristic that was present from nearly the moment that they were conceived. However, by adjusting and supplementing statistical and machine learning methods and concepts, we can diagnose and reduce the harm that they may otherwise cause.

In popular and technical writing, these issues are often captured by the general term “ethical data science.” We use that term here, but we also use the more general phrase “responsible data science.” Ethics can refer in some usages to narrow “rules of the road” that pertain to a particular profession, such as real estate or accounting. Our goal here is broader than that: presenting a framework for the practice of data science that is ethical, but not in a narrow sense: it is responsible.

The Optum Disaster

In 2001, the healthcare company Optum launched Impact-Pro, a predictive modeling tool. Impact-Pro was an early success for predictive analytics (predating the term data science), and a decade later, Steven Wickstrom, an Optum VP, touted its use cases. For healthcare providers, it could “support steerage to appropriate programs” and “identify members [patients] with gaps in care, complications, and comorbidities.” Optum termed these care opportunities in one document (i.e., opportunities for more revenue), but they are also of interest to those concerned with cost management: the correct early intervention in a health problem can cost significantly less than more drastic action later. For insurers, information on health risks for specific groups and individuals could be used to set premiums more accurately than is possible using traditional underwriting criteria.

DEFINITION DATA SCIENCE We use the term data science broadly to cover the process of understanding and defining a problem, gathering and preparing data, using statistical methods to answer questions, fitting models and assessing them, and deploying models in an organizational setting. We consider artificial intelligence (AI) to be part of data science, and we also consider the “science” component of data science to be important.

In 2019, though, a research team found that the tool was fundamentally flawed. For one important group—African Americans—the tool consistently underpredicted need for healthcare. The reason? The tool was essentially built to predict future spending on healthcare, and prior spending was a key predictor for that goal. And prior spending is a function not just of need, but also of ability to pay for and gain access to healthcare. Relative to other ethnic groups in the United States, African Americans have been (and continue to be) less insured, are less able to access healthcare, and possess fewer financial resources for covering healthcare expenses. In Optum's data, therefore, African Americans had less prior spending and, hence, less predicted future need. As a result, African Americans were less targeted for preventive intervention and necessary follow-up healthcare than were other people with similar health profiles. Neither the model nor the data provided to it were able to account for the unanticipated and overlooked societal inequities lurking beneath.

Optum was blindsided. The company thought it had built a tool that was a winner on all fronts: improving health outcomes by being smarter about required follow-up care, and managing costs better in the bargain. Instead, it found itself the focus of widespread bad publicity and was pilloried for creating a product that exacerbated racial bias and widened the healthcare gap faced by African Americans. New York state regulators opened an investigation, and the controversy continued into 2020. At the time of writing, Optum continues to market Impact Pro.

In this case, and in many others, the original intent for using the algorithm was good: good for healthcare providers by optimizing the allocation of scarce resources, and good for patients by ensuring that patients with the greatest needs had those needs met. But good intentions plus smart artificial intelligence (AI) led to disaster.

DEFINITION ARTIFICIAL INTELLIGENCE We use the term artificial intelligence generally, to cover both statistical and machine learning methods for prediction with structured numeric data and text, as well as image and voice recognition and synthesis. In this book, we think of AI as having underlying algorithms or models. When discussing solutions for reducing the harms of AI, changing these underlying algorithms or models will be one of the main focal points

Interestingly, the scenario of good statistics being ill-used is not new. In fact, statistics as a field has a long history of being used for nefarious purposes or causing unintended harms.

Jekyll and Hyde

Let's begin with a look back over a century in history to a classic work of fiction that serves as a metaphor for the issues we face with data science today. In his gothic tale The Strange Case of Dr. Jekyll and Mr. Hyde, Robert Louis Stevenson describes two characters. Dr. Jekyll is an analytical man of science, a great asset to society, and a doer of good deeds. However, there is a repulsive, cruel side to him in the form of a separate character, Mr. Hyde, who gets “released” from time to time. The evil Mr. Hyde, in his times of release, tramples a young girl, commits murder, and more. The phrase “Jekyll and Hyde” has come to represent something that has two contradictory but inextricably linked natures—one respected and upright, the other base and evil.

The dual nature of humanity—good and evil combined in the same package—is a universal theme in literature. As humans carry their intelligence into the artificial realm, this duality has come with it.

Artificial intelligence has taken on this Jekyll and Hyde character trait. The enormous benefits brought by AI are evident: it has been a major force powering economic growth over the last several decades. Most aspects of life and industry now incorporate AI approaches in some way. Here are just a few examples:

• When you apply for a loan or a credit card, it is an algorithm that judges whether the application should be approved. This speeds the process, lowers the cost of providing credit, and, by making the process more scientific, standardizes decisions and expands access to credit among the truly creditworthy.
• When you use Facebook, Instagram, Twitter, or other social media services, the ads you see are optimized by an ...

Table of contents

• Cover
• Table of Contents
• Title Page
• Introduction
• Part I: Motivation for Responsible Data Science and Background Knowledge
• Part II: The Responsible Data Science Process
• Part III: RDS in Practice
• Index
• Copyright
• About the Authors
• About the Technical Editor
• Acknowledgments
• End User License Agreement