2010

S12.E1 ∙ How The Lottery Was Stolen

Thu, Jan 27, 2022

Numbers don't exist in a vacuum, especially when they make up a game that's an essential part of a community. And sometimes they turn into a game so good that states and nations want it for themselves.

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S12.E2 ∙ Math Caught A Serial Killer

Mon, Feb 14, 2022

Kristen Gilbert was nicknamed the "Angel of Death" because so many patients happened to die during her shifts. No one saw her doing anything wrong, and there just wasn't any physical proof in post mortem examinations... but many of the patients who went into cardiac arrests didn't have health problems that should result in heart failure. Some were even young and physically healthy. But without hard evidence, is it even possible to determine whether a doctor or nurse is actually killing patients?

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S12.E3 ∙ The Bad Science of Eyewitnesses

Tue, Apr 19, 2022

Jennifer Thompson was the perfect witness. She was smart, perceptive, and alert during her attack. She made a conscious effort to note every little detail to help police identify the man who assaulted her. She identified him with 100% confidence in a photo lineup, then during an in-person lineup, and again at the trial. 11 years later, DNA evidence proved that Ronald Cotton, who had been sentenced to life in prison plus 50 years, was entirely innocent. How could she get it so wrong? Why do so many eyewitnesses make mistakes that result in innocent people going to prison for a crime they never committed?

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S12.E4 ∙ The Bad Math Used To Punish Criminals

Sun, Jun 19, 2022

Documentaries, television and movies are obsessed with crime. Is the accused actually guilty? If not, who is? If so, will they get away with it? All these questions matter -- but the most important question might be what happens when a criminal is sentenced.

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S12.E5 ∙ The Dangerous Math Used To Predict Criminals

Sun, Jul 24, 2022

The criminal justice system is overburdened and expensive. What if we could harness advances in social science and math to predict which criminals are most likely to re-offend? What if we had a better way to sentence criminals efficiently and appropriately, for both criminals and society as a whole?

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S12.E6 ∙ Why Mathematicians Won't Help Cops

Tue, Sep 06, 2022

Algorithms are so amazing at predicting what you want that ads will be perfectly tailored to your interests and displayed to you just minutes after a complex system has basically mapped your mind. If that's so easy, why can't police predict crimes? A few companies claim they can -- but the truth is a lot more complex.

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S12.E7 ∙ The Number That Gets You Shot

Sun, Oct 23, 2022

Imagine a world in which everything about your life -- your friends, your family, which school you went to, your social media activity -- are reduced to a simple number used by police and the government to determine whether something bad will happen to you.

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S12.E8 ∙ Math That Gets You Arrested

Sun, Nov 13, 2022

One of the most significant developments in the history of policing is the use of statistics to track crime patterns and to determine how to react to them. New York City's CompStat program has served as a model not just for cities around the United States, but also globally. And it makes sense, right? The better we track crimes and the more data we have, the more effectively we can allocate resources to improve public safety.

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S12.E9 ∙ Crime Stats Are A Lie

Sun, Nov 20, 2022

We've been collecting and analyzing social statistics for about 200 years now. In some ways, we've made a lot of progress -- but in others, we're just as clueless now as when André-Michel Guerry first set forth his laws of tracking crime. Mixing math and morality introduces so many possible variables that our interpretations of the data can range from insightful to harmful. Comparisons of violent crime between Scotland, India, and Estonia show just how difficult it is to make sense of social statistics, especially if we try to judge them in relative terms.

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S12.E10 ∙ The Problem With Talking To People

Mon, Nov 28, 2022

If you can't trust yourself, then who can you trust? Well... everyone and no one. The paradox of self-reporting in all manners of data collection, from basic census information to detailed stats about crime, makes it difficult for us to understand the problems we need to solve. Is it best to design experiments and carefully cull datasets to observe the information we need? Should we rely solely on testimony directly from people involved, and then trust that their answers are correct? Is the right answer a blend of the two?

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