THE CASE: PRE-CRIME, INC.
Predictive policing is being implemented by more and more law enforcement agencies across the United States. It is the practice of applying algorithms to large data setsbig datain order to better direct policing activities and resources in the community and in some cases even to identify the potential victims or perpetrators of crimes. The literary example most often cited in articles written about the subject is science fiction writer Philip K. Dicks 1956 short story, Minority Report. In it though, data-crunching computers are replaced by three clairvoyant humans, referred to as precogs. They could foresee crimes that had not yet been committed, who would do them, and to whom they would be done. As readers of Minority Report and viewers of the more recent movie and television series that it spawned are aware, there was a flaw in that fictional, human-centric system. But even mathematically driven predictors are far from flawless, at least the ones that have emerged over the past two decades. After all, these programs still depend on human beings to gather the data, pick which to use and how to analyze it, and decide how to apply the results in the real world. People are still the prime actors in the process. Starting in 1994, the New York Police Department began putting together data analysis of crime statistics throughout the eight boroughs of the city and used it to single out hotspots for crime. It focused on seven major index crimes such as murder, rape, armed robbery, etc. It was called CompStat, short for compare statistics. Its goal was to get information about hotspots into the hands of police commanders as quickly as possible so that police officers could be pre-positioned where they would most likely be needed. Before CompStat, crime reports were tallied every three months. By the time those reports reached managers, they were, literally, history and of little use. CompStat was more than a collection of charts and graphs. It was a management tool, used to develop best practices. Its success has spurred the adoption of predictive policing programs in sixty cities across the country. CompStat has been credited with lower crime statistics in New York City across almost all categories. For the most part, other police departments that employ predictive policing algorithmsrather than reinvent the wheel and in an effort to get up and running quicklyhave hired contracting firms which are usually relatively new businesses using their own proprietary software. Although these companies may lay claim to impressive improvements in crime statistics for their customers, few have been verified by independent studies. Their systems may depend on crime statistics from their client police agencies as well as information scraped from social media and other personal data accessible through the Internet. It could also incorporate video streams from surveillance cameras. One can speculate, but there is no way for the public to find out how information is being collected and analyzed, because company algorithms are trade secrets, essentially existing inside a black box.
PAPER PROMPTS:
Please Note: There are many interesting ethical issues embedded in this case, but for the purposes of this assignment please stay focused on the issues of epistemology – specifically on using induction as a reliable means of gaining knowledge.
How do you think Locke would respond to the reliability of these predictive analytics in policing like CompStat? In defending your response, include a brief explanation of Locke’s theory of epistemology. Be sure to explain what induction is and why Locke supports it as a means of gaining knowledge. (25 Points)
How do you think Hume would respond to the reliability of these predictive analytics in policing like CompStat? In defending your response, include a brief explanation of Hume’s skepticism and why he rejected the principle of induction. (25 Points)
With which of these two philosophers do you find yourself most inclined to agree? Briefly defend your answer. (10 Points)
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