The World's Best Book Summaries
How powerful is math? What’s the difference between benign math models and dangerous math models? In Weapons of Math Destruction, Cathy O’Neil notes that not all mathematical models are dangerous. When implemented responsibly, mathematical models can be powerful tools that enable organizations to make effective decisions that wouldn’t otherwise be possible. Below we’ll define mathematical models to show the power of math.
What’s the definition of mathematical models? Are math models biased toward a specific group of people? In Weapons of Math Destruction, data scientist Cathy O’Neil details the insidious ways mathematical models are being used to determine everything from interest rates to prison sentences. She contends that, while mathematical models can efficiently sort through vast amounts of information, they can produce dangerous results. Let’s take a look at O’Neil’s definition of a mathematical model.
What are the main challenges in data science? How can mathematical models be dangerous? A mathematical model is a mathematical simulation of a real-world event. Three characteristics make them dangerous: they’re opaque, they don’t incorporate feedback, and they operate on a large scale. Keep reading to learn more about the challenges in data science and math models.
What are the dangerous effects of statistics? How do statistics perpetuate social bias? Believe it or not, dangerous math models can have a grave impact on society. According to Cathy O’Neil, dangerous statistics disproportionately harm poor people, reproduce social bias, and make harmful self-fulfilling prophecies. Learn more about how statistics can negatively affect people and society when used incorrectly.
What causes big data discrimination? How do dangerous math models hurt the poor? In Weapons of Math Destruction, Cathy O’Neil argues that dangerous math models disproportionately inflict harm on the poor. This is because they show favoritism towards rich people. Let’s dive deeper into big data’s discrimination problem.
How can you use mathematical models ethically? What are ways to regulate industry use of math models? Cathy O’Neil proposes strategies industries and governments can take to limit the harm caused by dangerous mathematical models. She recommends monitoring math models, regulating them, and setting more positive goals for math models. Keep reading to learn more about ethics in mathematics.
Why are female sexual assault statistics high in America? Where is sexual assault and rape most likely to happen? As of 2014, Rebecca Solnit estimated that tens of millions of women are raped in America every year. Further, she notes that sexual assault and harassment tend to happen in workplace or educational settings where victims are sometimes held accountable for preventing these instances of violence. Learn more about why the statistics for female sexual assault are so high in America.
What are the statistics on female abuse? How does the patriarchy contribute to these growing numbers? According to Rebecca Solnit, women have long been fighting for basic human rights. Despite great progress in recent decades, the numbers on the mistreatment of women are still too high. Let’s look at the abuse of women statistics to get a better understanding of why women are still fighting for equality.
How much damage can improper data collection create? How can manipulators leverage it for their own advantage? In Calling Bullshit, Carl T. Bergstrom and Jevin D. West contend that bullshit often arises when data-based arguments rely on flawed data. They explain how the observation selection effect is an example of this and show how some people take advantage of it to deliberately deceive others. Continue reading to learn about the observation selection effect and how it can wreak havoc.
Should you trust data-based arguments? How can data go terribly wrong? In Calling Bullshit, Carl T. Bergstrom and Jevin D. West investigate how bullshit is created. They assert that it happens when people use faulty data as a basis for their arguments. Specifically, they say selection bias can lead to bullshit because it justifies faulty conclusions based on unrepresentative samples. Read more to understand how selection bias in statistics can lead to harmful misinformation.