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Book details
File Size: 2.64 MB
Format: pdf
Print Length: 260 pages
Publisher: Apress; 1 edition (June 7, 2019)
Publication Date: June 7, 2019
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07SRNX4HP

Are algorithms friend or foe?

The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favor, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias.

In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses some of the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors―and originates in―these human tendencies. Baer dives into topics as diverse as anomaly detection, hybrid model structures, and self-improving machine learning.

While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. You’ll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the impact of algorithmic bias on society and take an active role in fighting bias.

What You’ll Learn

Study the many sources of algorithmic bias, including cognitive biases in the real world, biased data, and statistical artifact
Understand the risks of algorithmic biases, how to detect them, and managerial techniques to prevent or manage them
Appreciate how machine learning both introduces new sources of algorithmic bias and can be a part of a solution
Be familiar with specific statistical techniques a data scientist can use to detect and overcome algorithmic bias
Who This Book is For

Business executives of companies using algorithms in daily operations; data scientists (from students to seasoned practitioners) developing algorithms; compliance officials concerned about algorithmic bias; politicians, journalists, and philosophers thinking about algorithmic bias in terms of its impact on society and possible regulatory responses; and consumers concerned about how they might be affected by algorithmic bias.

Table of contents (23 chapters)

Introduction
Bias in Human Decision-Making
How Algorithms Debias Decisions
The Model Development Process
Machine Learning in a Nutshell
How Real-World Biases Are Mirrored by Algorithms
Data Scientists’ Biases
How Data Can Introduce Biases
The Stability Bias of Algorithms
Biases Introduced by the Algorithm Itself
Algorithmic Biases and Social Media
Options for Decision-Making
Assessing the Risk of Algorithmic Bias
How to Use Algorithms Safely
How to Detect Algorithmic Biases
Managerial Strategies for Correcting Algorithmic Bias
How to Generate Unbiased Data
The Data Scientist’s Role in Overcoming Algorithmic Bias
An X-Ray Exam of Your Data
When to Use Machine Learning
How to Marry Machine Learning with Traditional Methods
How to Prevent Bias in Self-Improving Models
How to Institutionalize Debiasing