Drivers in suburban Virginia were shocked late last year when they were hit with $40 rush hour tolls from a new surge pricing system on a major commuter route into Washington. Boston parents were outraged when the public schools abruptly announced bell schedule changes that threatened to wreak havoc on carefully-timed family and work schedules. And in New York City, a departing City Council member recently won approval for a unique task force to study the freshest, and perhaps least understood, tool in government policymaking: algorithms like those that sparked sudden changes in commuter tolls and school schedules.
Increasingly, governments – federal, state and local – are turning to automated decision making systems to try to finetune their operations, save money, and increase efficiency. The public, however, has little understanding or access to information about how governments are using data, much of it collected quietly, to feed the algorithms that make decisions about everyday life. And, in an uncomfortable twist, the government agencies themselves often do not fully understand how algorithms influence their decisions. Officials in many jurisdictions are beginning to realize that automated decision making doesn’t necessarily deliver better outcomes or more equitable access to services.
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