“Weapons of Math Destruction” is a book about the financial crisis, and how math models have influenced our society in ways never intended. These algorithms are used to make all kinds of decisions that affect our lives – from who gets healthcare or even housing, to what we see on social media feeds. The author argues that these “weapons”, as he calls them, do more harm than good because they lack transparency and accountability for their power over us.
The “Weapons of Math Destruction” is a book about the dark side of big data. The book talks about how algorithms can be used to exploit people and make decisions that are not in their best interest.
Are you seeking for a synopsis of Cathy O’Neil’s book Weapons of Math Destruction? You’ve arrived to the correct location.
After reading Cathy O’Neil’s book, I wrote down a few significant takeaways.
If you don’t have time, you don’t have to read the whole book. This book synopsis gives you a quick rundown of all you can take away from it.
Let’s get this party started right now.
I’ll go through the following issues in my Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy book summary:
What is the purpose of Weapons of Math Destruction?
Weapons of Math Destruction is a critical examination of the growing number of algorithms that may have an influence on your daily life in ways you aren’t even aware of. As more professions are automated, unfavorable consequences on a rising number of individuals are being seen in schools and police agencies. Make sure you know everything you can do to safeguard yourself and your data.
Who wrote Weapons of Math Destruction and why?
Cathy O’Neil graduated from Harvard University with a Ph.D. in mathematics. She worked as a data scientist for different start-ups in the business sector after teaching at Barnard College. One of her most popular blogs is Mathbabe. She is also the author of Doing Data Science.
For Whom are Weapons of Math Destruction Designed?
The book Weapons of Math Destruction is not for everyone. It could be perfect for you if you are one of the following categories of people:
- Students and aficionados of computer science and statistics
- Protesters on the internet
- Readers who are worried about their right to privacy
Summary of the book Weapons of Math Destruction
Introduction
Most people are aware of big data and how algorithms based on it might reveal fresh information about consumer behavior, politics, and social media platforms. Algorithms may be found anywhere. They sift through social media feeds and adverts. They also have a significant impact in other elements of human existence, such as the employment and schools to which we have access.
It would be more equitable to make judgments – about employment or admittance – based on objective calculations rather than someone’s gut emotion. After all, algorithms assess everyone in the same way. As you’ll see in this book, it’s a little more complicated than that.
Lesson 1: Algorithms have the potential to alter public opinion and destabilize democratic systems.
The internet helps democracy in a variety of ways. It aids democracy in many ways as a forum for independent speech. Those same platforms, on the other hand, are susceptible to huge propaganda machinery that may skew the debate.
Algorithms in social media and search engines are especially prone to influencing the choices of unwitting users.
Epstein and Robertson found this to be true when they questioned indecisive voters in the United States and India for information on a variety of candidates.
The voters, on the other hand, were unaware that the search engine was built with an algorithm that preferred one candidate over the others. The votes of the participants swung 20% in favor of the algorithm’s decision.
Just before the 2012 elections, Solomon Messing of the Pew Research Center created a custom algorithm that produced the news feeds of two million users and prioritized political content above all other entries.
Facebook polled its users before and after the election, and the findings revealed that 3% more people voted than had been projected before Facebook’s algorithm was tweaked to favor politics.
Even though we can’t be sure whether the algorithms are meant to influence consumers, there is a great potential for abuse of particular search engine and social network algorithms.
Candidates are also well aware of their ability to sway public opinion.
Before the 2012 presidential election, the Obama team utilized data analysts to construct mathematical profiles by interviewing hundreds of voters and then utilizing their responses, as well as demographic and consumer data.
Similar persons were discovered in national databases based on these profiles. They were able to deduce from the profiles that persons with comparable interests and backgrounds had similar political beliefs.
After the analysts had categorized individuals with comparable data, they were able to design algorithms that assured these groups got adverts tailored to their preferences.
Those who shown an interest in the environment, for example, were targeted with advertisements touting Obama’s environmental programs.
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Lesson 2: Predictive algorithms may amplify preconceptions.
Today, it is possible to foresee future crimes, but it sounds like something from a Philip K. Dick science-fiction book. Algorithms are used by law enforcement to detect possible offenders.
However, the software isn’t flawless, and the algorithms have resulted in unequal policing in cities and discrimination against particular groups of individuals.
What was the catalyst behind this?
Based on past data, police choose which data is put into the algorithm, which pinpoints where crimes are most likely to occur.
There’s also the issue of police focusing on certain sorts of crimes, such as “nuisance crimes” like vagrancy and drug-related charges.
Because these crimes tend to occur in disadvantaged communities, the study will be heavily slanted toward them. As a result, the majority of police patrol units are stationed in impoverished areas, making locals feel unjustly targeted. In addition, wealthy areas are neglected, making them more prone to crime.
Built-in biases are also used by police to distort data regarding possible violent crimes, causing innocent persons to be labeled as dangerous.
The Chicago Police Department was awarded a grant in 2009 to create new crime-prediction software. They used the funds to create an algorithm that generated a list of the 400 most probable murder suspects.
One of those guys, 22-year-old Robert McDaniel, was the target of police scrutiny. In 2013, a police officer came up to McDaniel’s residence to notify him that he was being watched by the authorities.
McDaniel, on the other hand, was never charged with a crime. Finally, the system highlighted him based on the individuals he follows on social media as well as local criminals.
If you’re growing up, all you need is a bad neighborhood to be called dangerous.
Despite their best efforts, crime prediction algorithms may do more harm than good to people’s lives.
As we’ll see in the following chapter, the insurance business has a similar issue.
Lesson 3: Insurance firms take advantage of people with bad credit.
You may be aware that various agencies charge different customers varying rates. This isn’t something that happens at random. The information they gather about their clientele determines their charges.
The amount a consumer pays for vehicle insurance is determined by algorithms that include how many accidents they’ve had in the past as well as their credit history.
Credit reports are even given more weight in certain regions than driving records.
In Florida, for example, persons with clean driving histories but low credit scores pay $1,552 more per year on average than those with good credit but no prior drunk driving convictions.
As a consequence, insurance prices for impoverished drivers with excellent driving abilities are higher than those for wealthy drivers.
Families that are short on cash are more likely to skip an insurance payment, lowering their credit score and forcing them to pay more for insurance. When their current contract ends, their insurance premium will go up, even if they have never violated a traffic regulation.
Many insurance firms utilize algorithms to predict whether or not a consumer is likely to shop for a better deal.
Allstate does this by using a model based on customer and demographic data. If the algorithm shows that the client is likely to hunt for cheaper costs, a corporation may cut a pricing by up to 90% off the average.
If a consumer is unlikely to shop around, on the other hand, his rate might increase by 800 percent.
Allstate’s algorithm, on the other hand, is designed to target impoverished individuals without a formal education, who are less inclined to shop around for alternatives.
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Lesson 4: Algorithms also have an unfavorable impact on the job market.
Choosing the best personnel might be challenging when there are hundreds of candidates. It makes logical to use a number of tests in conjunction with data businesses to filter through the findings.
Certain types of exams, notably personality assessments, have shown to be restricted, making finding work almost hard for someone like Kyle Behm.
Behm dropped out of Vanderbilt University to seek treatment for his bipolar condition. In 2012, his health permitted him to begin searching for a part-time employment.
Behm learned about a job vacancy at the Kroger grocery company through a friend. He submitted an application. When he was turned down, he checked with a friend and learned that he had been “red-lighted” due to his personality test results, with the algorithm deeming Behm “likely to underperform.”
The same scenario occurred every time Behm applied for a minimal wage job. With the support of his father, he sued seven separate firms under the Americans with Disabilities Act. In 2016, the lawsuit is still ongoing.
The firms that handle the data may make some worrisome errors.
After being charged with intent to make and distribute methamphetamine, Catherine Taylor was turned down for a position with the Red Cross in Arkansas. Catherine was taken aback by this since her record was quite spotless.
As she dug further, she learned that the accusations belonged to a different Catherine Taylor, who happened to be born on the same day as her.
After conducting some further investigation, she learned that the business that supplied the data to the Red Cross had made a mistake. Catherine later discovered that eleven other data brokers had made the same mistake, linking her to a major crime she had never committed.
Lesson 5: University rankings have a detrimental impact on higher education.
Few people are aware that a newspaper has had a big part in the rise in college tuition costs in the United States over the last 30 years.
In the 1980s, a US News and World Report algorithm started rating US universities based on statistics they thought would influence their performance, such as SAT scores and admission rates.
All of the institutions participating recognized the significance of this rating and set out to improve their performance in the categories evaluated by US News. For this to happen, resources were required.
Tuition has risen dramatically as a result of the money crunch. Between 1985 and 2013, the expense of higher education climbed by 500%.
Although rankings were not the primary factor affecting the fee rise, they did push colleges to do so.
One of the most detrimental things Usnews did was include acceptance rates in their formula, which undermined the image of a “safety school.”
A safety school, according to tradition, was a college with a high acceptance rate that acted as a backup plan for students planning to apply to more prominent colleges such as Harvard or Yale.
Many colleges responded by decreasing their acceptance rates and sending out fewer acceptance letters, since US News ranked institutions with a lower acceptance rate higher.
In order to maintain the same enrollment numbers, they had to turn down certain students. When the safety schools looked at their data, they realized that only a tiny fraction of elite students would select them over prominent colleges, so they chose to turn them down.
Even if just a tiny number of these high-achieving pupils attended, the institution would have benefited. Furthermore, the choice to reject top achievers outright wrecked many decent students’ backup plans.
Despite starting off as a nice notion, all of the algorithms we looked at ended up doing significantly more damage than good.
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Final Thoughts
By avoiding all-too-human prejudices, algorithms were built to avoid human biases and incorrect reasoning. Many algorithms used today, in fields ranging from insurance to legal processes, reflect the preconceptions and misunderstandings of their developers. Because these algorithms function on such a large scale, this results in millions of biased choices.
Additional Reading
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The “Weapons of Math Destruction” is a book that looks into how data has been used in the past and what it means for society. The author, Cathy O’Neil, discusses how data can be weaponized to manipulate people’s opinions or actions. Reference: weapons of math destruction chapter 2 summary.
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