In 2016 a group of scientists at MIT created an online platform to gather information about how people would decide what the outcomes of the actions of autonomous, automated systems “ought” to be. Although there were different scenarios, the most famous is the self-driving car that in the face of an imminent accident had to “decide” who should be run over and killed and who should be spared. Since it was matter of making decisions about what ought to be done in a case that led to harms, this was called a “moral” machine. The “machine” part comes from the fact that the automatic system was to be programmed in advance, which choice to make, that is, the choice was no longer “free” as would be the case when a human driver made the decision, but was determined by the programmer, who then bore the moral responsibility.

More interesting than the results of this experiment (see http://moralmachine.mit.edu/) are the assumptions it makes. One important assumption is that there are no accidents, that is, the fact that someone will be killed in an “accident” is not accidental, but a determined outcome of programming. Not just anything could happen, but only certain things could happen, and among these the choice was to be made in advance so that what does happen, happens “mechanically.” The second important assumption is that the future is no longer open and the present no longer free. Usually, we assume that the past is certain, the present is free, that is, we can decide in the present moment what to do, and the future, that is, the consequences of our actions, is open. We don’t know what the future brings. The future is contingent. This age-old temporal scheme is placed in question by the moral machine. The idea is that data analytics is able to know what will happen in the future and on the basis of this knowledge interventions in the present can be made that will influence, indeed, determine which future options will be realized. This is called datafication. Datafication is 1) the process by which all present states of the world are turned into data creating thereby a virtual double of reality, 2) subjecting this data to descriptive, predictive, preventive, and prescriptive analytics so that the effects of all possible variables can be simulated and on the basis of data-based projections of what will happen, interventions in the present can be made to influence future outcomes. Datafication is the basis of intelligent, autonomous, automated systems, such as self-driving cars, but also personalized medicine, learning analytics in education, business intelligence in the private sector, and much more. This is what makes the moral machine interesting. It is a parable of the digital age and poses central questions about what it means to live in a datafied world.