Cost Of Human Life
By: Shannon Frances Smith
Donald Smith got up at his usual time of six in the morning to go to work. He tossed on a plaid dress shirt and jeans, the programmer’s unofficial uniform. Getting his keys for his late model car and his wallet, he walked out the sliding door of his one-bedroom condo.
He was part of a development team at Canal Railroads. They were creating an AI to control the trains and rail switches. The AI’s purpose was to automate the trains’ running by making decisions usually made by an operator or engineer. The AI was in its testing phase, and on this day, Donald was tasked to put it to the ultimate challenge: The Trolley Problem.
The Trolley Problem refers to an exercise in ethics that goes like this: you have a runaway trolley going towards five people. The trolley can be diverted away, but the other track has one person on it. The trolley does not have time to stop for either. Do you divert the train?
This test was necessary for this very scenario could happen with actual trains. The algorithm had to do the right thing based on the information that had been fed into it. The AI’s results doing the wrong thing would be the possible death of many and the loss of millions of dollars from train damage and lawsuits.
Donald got to his work station at the usual time to punch in the parameters. The desk itself was black with a white plastic surface where his computer tower and two monitors were sitting with a black swivel chair waiting for him. He saw various others from his team typing away at their stations, monitoring one thing or creating a change list for the other. A few business-dressed people walked by and exchanged words he did not understand before walking towards the breakroom.
Into the terminal he entered the data: five persons on a track near Kirby with a switch and control on the track ahead had one person on the diverted route. He punched in that the weather was a sunny day about twenty degrees Celsius, the track ran through a field and there was no damage to the rails. He also put in that it was noon on a typical Wednesday. These details might seem a bit much, but the model asked them as part of its decision making.
He waited for the result. In live production, this data would already be known or sensed by the AI. He entered the mock data and waited with a smug grin. “This is easy,” he thought. “Kill one to save five.”
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