Weapons of Math Destruction Summary, Review PDF

The book Weapons of Math Destruction examines the growing number of algorithms that may have an impact on your daily life in ways you are unaware of. As more jobs are automated, schools and police departments are experiencing negative effects on an increasing number of people. Make sure you learn what you can do to protect yourself and your data.

You may be wondering if you should read the book. This book review will tell you what important lessons you can learn from this book so you can decide if it is worth your time.

At the end of this book review, I’ll also tell you the best way to get rich by reading and writing

Without further ado, let’s get started. 

Weapons of Math Destruction Summary

Lesson 1: Biases are reinforced by predictive algorithms.

It may sound like a Philip K. Dick science fiction novel, but it is now possible to predict when criminal acts will be committed. Criminal suspects are often tracked down with the help of algorithms used by law enforcement agencies.

The algorithms in the imperfect software have led to discrimination against some groups of city residents and unequal policing.

This is strange; how did this happen?

Crime hot spots can be predicted using an algorithm, and police can choose what information to feed into the system based on past records.

Vagrancy and other “nuisance crimes,” as well as certain drug offenses, receive disproportionate attention from police.

Analysis will be heavily weighted toward low-income neighborhoods, as these crimes are most prevalent there. As a result, the majority of police patrol units are assigned to low-income areas, which can lead to residents feeling unfairly treated. In addition, affluent communities are more likely to be targeted for crime because they are neglected.

Similarly, nonviolent offenders are misclassified as offenders due to police officer bias.

In 2009, the Chicago Police Department received a grant to fund the development of crime prediction software. This funding was used to develop an algorithm that generated a list of 400 people who were considered the most likely murder suspects.

Robert McDaniel, 22, was named by police as one of those involved. A police officer visited McDaniel at his home in 2013 to tell him he was being monitored.

However, McDaniel was never criminally charged for his involvement. Ultimately, the algorithm tracked him down because of his connections to online criminals and local wrongdoers.

The mere fact that he grew up in a bad neighborhood is enough to earn him a reputation as a dangerous person.

Crime prediction algorithms, despite their best intentions, can do more harm than good.

The insurance sector, as we will see in the next chapter, is struggling with the same problem.

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Lesson 2: Insurance companies take advantage of customers with low credit scores.

You probably know that the rates that different agencies charge their customers vary widely. This is not a random phenomenon. The rates are based on the data they collect about their customers.

To determine a customer’s monthly premium, insurance companies use complex algorithms that take into account the customer’s driving history and financial situation.

In some states, credit reports are weighted more heavily than driver’s licence information.

Adults in Florida who have neither driven under the influence nor have an excellent credit score pay $1,552 more annually for their car insurance than those who have neither.

So risk-taking drivers with low incomes pay more for their car insurance than those with higher incomes, even if they both drive similarly.

More expensive insurance policies are more likely to lead to delinquencies and a poorer credit score for families with scarce resources. Their insurance premium will increase when their current policy expires, even if they have never violated a traffic law.

Many insurance companies use algorithms to determine if a customer will look elsewhere for a cheaper rate.

Allstate uses a model that takes into account consumer and demographic information to do this. A company can lower a price by up to 90 percent below the market average if an algorithm predicts that a customer will actively seek cheaper alternatives.

In contrast, a customer’s rate can increase by up to 800% if they do not shop around.

Poor people without education are a prime target for Allstate’s algorithm because they are less likely to shop around for cheaper rates.

Lesson 3: Algorithms have an unfair impact on the labor market.

It can be difficult to find the best candidates among hundreds of applications. Sorting the results makes more sense if you use multiple tests in conjunction with data companies.

Some tests, especially personality tests, have proven to be extremely limiting and make it nearly impossible for someone like Kyle Behm to find employment.

Behm dropped out of Vanderbilt to get help for his bipolar disorder. In 2012, he was finally well enough to look for a part-time job.

A friend told Behm about an opening at the Kroger grocery chain. He applied for the job and we received his application. After being rejected, he did some research and found that the results of his personality test had resulted in a “red light”; the algorithm had determined that Behm was “likely to underperform.”

Behm was unsuccessful at every minimum-wage job he applied for. Along with his father, he filed seven separate ADA lawsuits against different companies. The case is still ongoing as of 2016.

There is a risk that the data-processing companies will make mistakes.

After Catherine Taylor was arrested for conspiracy to manufacture and sell methamphetamine in Arkansas, the Red Cross terminated her employment. Catherine found this surprising given her relatively unblemished criminal history.

She investigated and found that the charges belonged to another Catherine Taylor, who also happened to have her birthday.

Digging deeper, she discovered that the Red Cross data vendor had made a mistake. Ten data brokers had made the same mistake as Catherine, accusing her of a serious crime she had never committed.

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Lesson 4: College rankings have a negative impact on higher education.

Few people know that a newspaper has been a major factor in the 30 percent increase in tuition in the U.S. during this period.

U.S. News and World Report began using an algorithm in the 1980s to rank American universities based on factors such as SAT and admission rates, which they believed were an indicator of each institution’s success.

They all knew how much that ranking meant to their reputation, so they all worked hard to improve their scores in the categories US News examined. Money was needed for this purpose.

As a result of financial uncertainty, tuition skyrocketed. The price of a college education increased 500% between 1985 and 2013.

Schools were motivated to raise prices by factors other than ranking, but the latter certainly had an impact.

The inclusion of admission rates in Usnews’ formula was particularly damaging because it rendered the concept of a “safe school” obsolete.

The term “safety school” historically referred to a college with a high acceptance rate that students could attend if they were rejected by more selective institutions such as Harvard or Yale.

As a result of US News ranking schools with lower acceptance rates higher, many institutions began accepting fewer applicants and sending out fewer admission letters.

In order to enroll the same number of students, they had to reject some applications. When the safe schools looked at their data, they found that only a small percentage of the top students would choose them over the elite schools.

The school would have benefited even if only a fraction of these high-achieving students had applied. Not only that, but the decision to turn away high-performing students destroyed the plans of many excellent students.

All of the algorithms we examined were well-intentioned, but they ended up doing more harm than good.

Weapons of Math Destruction Review

Weapons of Math Destruction is a great book I’d like to recommend to anyone who is interested in data science. If you spend some time digesting the ideas, it might make a positive impact on your life.

Unlike humans, algorithms are not prone to bias or illogic, so they were designed to be objective decision-makers. However, many current algorithms used in fields ranging from insurance to the legal system have adopted the biases and misconceptions of their designers. These algorithms make millions of decisions at once, and their inherent biases cause much injustice.

Nowadays, automated resume readers are used by many employers. If you want to improve your chances of getting hired, you should adapt your resume for the robot reader. These are some basic rules that always apply:

About the Author

Harvard University awarded Cathy O’Neil a Ph.D. in mathematics. She taught at Barnard College before working as a data scientist for various private sector start-ups. One of her most popular blogs is Mathbabe. She is also the author of the book Doing Data Science.

Buy The Book: Weapons of Math Destruction

If you want to buy the book Weapons of Math Destruction, you can get it from the following links:

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