Solutions to Reinforced Feedback Loops in Machine Learning-based Predictive Policing
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2020-01-13
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en
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Abstract
Predictive Policing is an increasingly popular prediction system used by police
all over the world to make tactical decisions on patrolling based on historical
crime data of a variation of geographical locations. Results are considered to be
positive; however, the proven downside of the software is reinforced feedback
loops. Increased patrolling rates in a location, likely finding crime, and reporting
back to the algorithm that indeed crime has been found can result in bias
against certain locations and its inhabitants. Using a simplified implementation
of a predictive system, countermeasures that can reduce this affect are tested
and judged on their effectiveness. The successful countermeasures consist of
connecting population numbers to crime rates, using precincts instead of geographical
locations, and/or adding more attributes like type of crime. It is important
to be aware of ways to reduce a feedback loop in these location-based
systems without losing performance.
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Faculteit der Sociale Wetenschappen