Foot already anticipates the missing context of her cases, even before the tram example became the trolley problem. “In real life,” she writes, “it would hardly ever be certain that the man on the narrow track would be killed. Perhaps he might find a foothold on the side ... and cling on as the vehicle hurtled by.” One solution to this infinity of possibilities is just to run an infinity of trolley problems, gleaning patterns from the public response to them. That’s the Moral Machine’s approach, one that matches the way machine-learning systems work best: with a large data set. But another approach would involve considering specific problems in the most appropriate moral context.

As it happens, Foot offers a different example that shares more in common with what actually transpired in Tempe than the trolley does. Imagine five patients in a hospital. Their lives could be saved by being administered a certain gas, but the use of it releases lethal fumes into the room of another patient, who cannot be moved. In this case, the calculus of effect is identical to the classic trolley problem, and yet, to many the conclusion is not nearly so obvious. That’s just because of a difference between intended and foreseeable effect, but also because the moral desire to avoid causing injury operates differently.

In the trolley problem, the driver is faced with a conflict between two similar harms, neither of which he or she chooses. But in the hospital-gas example, the doctor is faced with a conflict between delivering aid and causing harm. In truth, Uber’s situation is even more knotted, because none of the parties involved seemed to possess sufficient knowledge of the vehicle’s current (not future) capacity for doing harm—not the company that makes the car, the driver who operates it, or the government that regulates it. That makes the moral context for the Uber crash less about the future of vehicular casualty, and more about the present state of governmental regulation, corporate disclosure, and transportation policy. But those topics are far less appealing to think about than a runaway trolley is.

If it’s a precedent in moral philosophy that technologists, citizens, and policy makers really want, they might do better to look at Uber’s catastrophe as an example of moral luck, an idea proposed by the philosopher Thomas Nagel. Here’s a classic example: An intoxicated man gets in his car to drive home at night. Though drunk, he reaches his destination without incident. Now consider a man in the same circumstances, but while driving he strikes and kills a child who crosses the road unexpectedly. It seems natural to hold the latter man more blameworthy than the former, but both took the same voluntary actions. The only difference was the outcome.

Seen in this light, the Uber fatality does not represent the value-neutral, or even the righteous, sacrifice of a single pedestrian in the interest of securing the likely safety of pedestrians in a hypothetical future of effective, universally deployed robocars. Instead, it highlights the fact that positive outcomes—safer cars, safer pedestrians, and so on—might just as easily be functions of robocars’ moral luck in not having committed a blameworthy act. Until now, of course.