We humans err and err often. If it is not a small mistake like leaving the keys in the fridge, then it is a deadly one like leaving the oven on all day. We tend to be reckless, forgetful, overconfident, easily distracted — dangerous traits when steering a two-ton, metal machine across lanes at 70 mph. Four out of the top five causes for car crashes are the result of human error.
Computers, on the other hand, have purely pragmatic minds. They sense data and react in programmed, calculated ways. Self-driving cars already seem to be safer than humans behind the wheel. The rate of progress in artificial intelligence over the past few years has some experts claiming that driving a car will be made illegal by 2030.
“Machine intelligence may have to deal with situations where someone has to die so someone else can live.”
But, as most drivers know, driving can require split-second decisions with no obvious right answer. A squirrel darts into the road — do you swerve and risk hitting other cars or drive straight and hope the squirrel survives. How would you react if a dog ran into the road? Or a criminal? Or a child? Which lives are worth risking? These questions are being asked by teams of researchers around the world. Now they are looking to you for answers.
“Self-driving cars are now practically inevitable,” Massachusetts Institute of Technology graduate student and research assistant Sohan Dsouza told Digital Trends. “That is a good thing, generally, because they would help save countless lives now being lost daily due to human driver error and can offer independent mobility to countless others who cannot drive.”
To that end, Dsouza, Edmond Awad, and their team at the MIT Media Lab developed Moral Machine, a platform that engages users in moral dilemmas and asks them how the self-driving car should respond. A handful of factors play into each scenario, including the age and gender of victims, their social status, and whether they are breaking the law. Participants are asked to make decisions in 13 dilemmas. The results then pooled as crowdsourced data and may one day be used to guide the development of ethical machines. After judging the dilemmas, users can compare their outcomes to others’ and even design their own for others to answer.
“One of our primary goals is provoking debate among the public,” Dsouza said, “and especially dialogue among users, manufacturers, insurers, and transport authorities.
“Not all crashes can be avoided, and the possibility remains that machine intelligence piloting vehicles may have to deal with situations where someone has to die so someone else can live — rather like the classic philosophical thought experiment known as the trolley problem.”
The trolley problem has been pondered for nearly 50 years. In it, a train car is en route to hit five people down the track. You have a switch that can steer the trolley down another set of tracks, where it will hit only one person. Would you intervene or do nothing?
“There are very few experiment-based studies regarding this possibility,” Dsouza said. “Hence, we needed to create a platform that would be able to generate large numbers of multi-factor scenarios and present them to users in an easy-to-understand, easy-to-use, and engaging way, so as to build a model of how people perceive the morality of machine-made decisions.”
“One of our primary goals is provoking debate among the public.”
Moral Machine has gathered answers on more than 11 million scenarios so far. Although the team has yet to perform a deep analysis, they are noticing regional trends that hint at the rocky road ahead. “On average, respondents from western countries place a relatively higher value on minimizing the number of overall casualties — that is, they approve more utilitarian choices — compared to respondents from eastern countries,” Dsouza said.
Revealing these cultural discrepancies fosters debate and dialogue, which is essential to making progress. “We believe we have already made an impact,” Dsouza said. “This dialogue will eventually help the stakeholders in this scene reach an equilibrium of legislation, liability assessment, moral comfort, and public safety.”
