Algorithm leads to arrest of the wrong person

Even though there was supposedly a person in the decision-making process and a surveillance photo wasn’t actually Robert Julian-Borchak Williams, he still ended up handcuffed in front of his own home. Kashmir Hill reporting for The New York Times:

This is what technology providers and law enforcement always emphasize when defending facial recognition: It is only supposed to be a clue in the case, not a smoking gun. Before arresting Mr. Williams, investigators might have sought other evidence that he committed the theft, such as eyewitness testimony, location data from his phone or proof that he owned the clothing that the suspect was wearing.

In this case, however, according to the Detroit police report, investigators simply included Mr. Williams’s picture in a “6-pack photo lineup” they created and showed to Ms. Johnston, Shinola’s loss-prevention contractor, and she identified him. (Ms. Johnston declined to comment.)

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Unsolved killings mapped

Thousands of homicides. Some cases result in an arrest. Many end up unsolved. The Washington Post mapped areas in major cities to show the contrast between the two types of homicide cases.

The data looks noisy at first, but when you compare cities like Baltimore with low arrest rates against cities like Atlanta with high arrest rates, you start to wonder.

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